Publications

Articles

  • H. Dias de Mello Jr, L. Martí, A. V. Abs da Cruz, and M. Rebuzzi Vellasco, “Evolutionary algorithms and elliptical copulas applied to continuous optimization problems,” Information Sciences, vol. 369, pp. 419-440, 2016. doi: 10.1016/j.ins.2016.07.006 bibtex abstract
    @article{harold-2016:copulas,
    Author = {Dias de Mello Jr, Harold and Mart\'i, Luis and Abs da Cruz, Andr\'e V. and Rebuzzi Vellasco, Marley~M.~B.},
    Journal = {Information Sciences},
    doi = {10.1016/j.ins.2016.07.006},
    month = {11},
    volume = {369},
    pages = {419--440},
    year = {2016},
    issn = {0020-0255},
    Title = {Evolutionary algorithms and elliptical copulas applied to continuous optimization problems},
    Year = {2016},
    abstract = {Abstract Estimation of Distribution Algorithms (EDAs) constitutes a class of evolutionary algorithms that can extract and exploit knowledge acquired throughout the optimization process. The most critical step in the EDAs is the estimation of the joint probability distribution associated to the variables from the most promising solutions determined by the evaluation function. Recently, a new approach to EDAs has been developed, based on copula theory, to improve the estimation of the joint probability distribution function. However, most copula-based EDAs still present two major drawbacks: focus on copulas with constant parameters, and premature convergence. This paper presents a new copula-based estimation of distribution algorithm for numerical optimization problems, named EDA based on Multivariate Elliptical Copulas (EDA-MEC). This model uses multivariate copulas to estimate the probability distribution for generating a population of individuals. The EDA-MEC differs from other copula-based EDAs in several aspects: the copula parameter is dynamically estimated, using dependence measures; it uses a variation of the learned probability distribution to generate individuals that help to avoid premature convergence; and uses a heuristic to reinitialize the population as an additional technique to preserve the diversity of solutions. The paper shows, by means of a set of parametric tests, that this approach improves the overall performance of the optimization process when compared with other copula-based EDAs and with other efficient heuristics such as the Covariance Matrix Adaptation Evolution Strategy (CMA-ES).}
    }
    Abstract Estimation of Distribution Algorithms (EDAs) constitutes a class of evolutionary algorithms that can extract and exploit knowledge acquired throughout the optimization process. The most critical step in the EDAs is the estimation of the joint probability distribution associated to the variables from the most promising solutions determined by the evaluation function. Recently, a new approach to EDAs has been developed, based on copula theory, to improve the estimation of the joint probability distribution function. However, most copula-based EDAs still present two major drawbacks: focus on copulas with constant parameters, and premature convergence. This paper presents a new copula-based estimation of distribution algorithm for numerical optimization problems, named EDA based on Multivariate Elliptical Copulas (EDA-MEC). This model uses multivariate copulas to estimate the probability distribution for generating a population of individuals. The EDA-MEC differs from other copula-based EDAs in several aspects: the copula parameter is dynamically estimated, using dependence measures; it uses a variation of the learned probability distribution to generate individuals that help to avoid premature convergence; and uses a heuristic to reinitialize the population as an additional technique to preserve the diversity of solutions. The paper shows, by means of a set of parametric tests, that this approach improves the overall performance of the optimization process when compared with other copula-based EDAs and with other efficient heuristics such as the Covariance Matrix Adaptation Evolution Strategy (CMA-ES).
  • N. Sanchez-Pi, L. Martí, and A. C. Bicharra Garcia, “Improving ontology-based text classification: An occupational health and security application,” Journal of Applied Logic, vol. 17, pp. 48-58, 2016. doi: 10.1016/j.jal.2015.09.008 bibtex abstract url
    @article{sanchez-2016:occupational,
    Abstract = {Abstract Information retrieval has been widely studied due to the growing amounts of textual information available electronically. Nowadays organizations and industries are facing the challenge of organizing, analyzing and extracting knowledge from masses of unstructured information for decision making process. The development of automatic methods to produce usable structured information from unstructured text sources is extremely valuable to them. Opposed to the traditional text classification methods that need a set of well-classified trained corpus to perform efficient classification; the ontology-based classifier benefits from the domain knowledge and provides more accuracy. In a previous work we proposed and evaluated an ontology-based heuristic algorithm for occupational health control process, particularly, for the case of automatic detection of accidents from unstructured texts. Our extended proposal is more domain dependent because it uses technical terms and contrast the relevance of these technical terms into the text, so the heuristic is more accurate. It divides the problem in subtasks such as: (i) text analysis, (ii) recognition and (iii) classification of failed occupational health control, resolving accidents as text analysis, recognition and classification of failed occupational health control, resolving accidents.},
    Author = {Nayat Sanchez-Pi and Luis Mart\'{i} and Bicharra Garcia, Ana Cristina},
    Doi = {10.1016/j.jal.2015.09.008},
    Issn = {1570-8683},
    Journal = {Journal of Applied Logic},
    volume = {17},
    pages = {48--58},
    year = {2016},
    Title = {Improving ontology-based text classification: {A}n occupational health and security application},
    Url = {http://www.sciencedirect.com/science/article/pii/S1570868315000774}
    }
    Abstract Information retrieval has been widely studied due to the growing amounts of textual information available electronically. Nowadays organizations and industries are facing the challenge of organizing, analyzing and extracting knowledge from masses of unstructured information for decision making process. The development of automatic methods to produce usable structured information from unstructured text sources is extremely valuable to them. Opposed to the traditional text classification methods that need a set of well-classified trained corpus to perform efficient classification; the ontology-based classifier benefits from the domain knowledge and provides more accuracy. In a previous work we proposed and evaluated an ontology-based heuristic algorithm for occupational health control process, particularly, for the case of automatic detection of accidents from unstructured texts. Our extended proposal is more domain dependent because it uses technical terms and contrast the relevance of these technical terms into the text, so the heuristic is more accurate. It divides the problem in subtasks such as: (i) text analysis, (ii) recognition and (iii) classification of failed occupational health control, resolving accidents as text analysis, recognition and classification of failed occupational health control, resolving accidents.
  • L. Martí, J. García, A. Berlanga, and J. M. Molina, “MONEDA: scalable multi-objective optimization with a neural network-based estimation of distribution algorithm,” Journal of Global Optimization, vol. 66, iss. 4, pp. 729-768, 2016. doi: 10.1007/s10898-016-0415-7 bibtex abstract
    @article{marti-2016:moneda,
    Abstract = {The extension of estimation of distribution algorithms (EDAs) to the multi-objective domain has led to multi-objective optimization EDAs (MOEDAs). Most MOEDAs have limited themselves to porting single-objective EDAs to the multi-objective domain. Although MOEDAs have proved to be a valid approach, the last point is an obstacle to the achievement of a significant improvement regarding ``standard'' multi-objective optimization evolutionary algorithms. Adapting the model-building algorithm is one way to achieve a substantial advance. Most model-building schemes used so far by EDAs employ off-the-shelf machine learning methods. However, the model-building problem has particular requirements that those methods do not meet and even evade. The focus of this paper is on the model-building issue and how it has not been properly understood and addressed by most MOEDAs. We delve down into the roots of this matter and hypothesize about its causes. To gain a deeper understanding of the subject we propose a novel algorithm intended to overcome the drawbacks of current MOEDAs. This new algorithm is the multi-objective neural estimation of distribution algorithm (MONEDA). MONEDA uses a modified growing neural gas network for model-building (MB-GNG). MB-GNG is a custom-made clustering algorithm that meets the above demands. Thanks to its custom-made model-building algorithm, the preservation of elite individuals and its individual replacement scheme, MONEDA is capable of scalably solving continuous multi-objective optimization problems. It performs better than similar algorithms in terms of a set of quality indicators and computational resource requirements.},
    Author = {Mart\'i, Luis and Garc\'ia, Jes{\'u}s and Berlanga, Antonio and Molina, Jos{\'e} Manuel},
    Doi = {10.1007/s10898-016-0415-7},
    Issn = {1573-2916},
    Journal = {Journal of Global Optimization},
    volume = {66},
    number = {4},
    Pages = {729--768},
    Title = {{MONEDA}: scalable multi-objective optimization with a neural network-based estimation of distribution algorithm},
    Year = {2016}
    }
    The extension of estimation of distribution algorithms (EDAs) to the multi-objective domain has led to multi-objective optimization EDAs (MOEDAs). Most MOEDAs have limited themselves to porting single-objective EDAs to the multi-objective domain. Although MOEDAs have proved to be a valid approach, the last point is an obstacle to the achievement of a significant improvement regarding “standard” multi-objective optimization evolutionary algorithms. Adapting the model-building algorithm is one way to achieve a substantial advance. Most model-building schemes used so far by EDAs employ off-the-shelf machine learning methods. However, the model-building problem has particular requirements that those methods do not meet and even evade. The focus of this paper is on the model-building issue and how it has not been properly understood and addressed by most MOEDAs. We delve down into the roots of this matter and hypothesize about its causes. To gain a deeper understanding of the subject we propose a novel algorithm intended to overcome the drawbacks of current MOEDAs. This new algorithm is the multi-objective neural estimation of distribution algorithm (MONEDA). MONEDA uses a modified growing neural gas network for model-building (MB-GNG). MB-GNG is a custom-made clustering algorithm that meets the above demands. Thanks to its custom-made model-building algorithm, the preservation of elite individuals and its individual replacement scheme, MONEDA is capable of scalably solving continuous multi-objective optimization problems. It performs better than similar algorithms in terms of a set of quality indicators and computational resource requirements.
  • L. Martí, J. García, A. Berlanga, and J. M. Molina, “A Stopping Criterion for Multi-Objective Optimization Evolutionary Algorithms,” Information Sciences, vol. 367–368, pp. 700-718, 2016. doi: 10.1016/j.ins.2016.07.025 bibtex abstract url
    @article{marti-2016:stopping,
    Author = {Mart{\'i}, Luis and Garc{\'i}a, Jes{\'u}s and Berlanga, Antonio and Molina, Jos{\'e} Manuel},
    Journal = {Information Sciences},
    doi = {10.1016/j.ins.2016.07.025},
    volume = {367–368},
    pages = {700--718},
    year = {2016},
    issn = {0020-0255},
    month = {7},
    url = {http://www.sciencedirect.com/science/article/pii/S0020025516305072},
    Title = {A Stopping Criterion for Multi-Objective Optimization Evolutionary Algorithms},
    abstract = {Abstract This paper puts forward a comprehensive study of the design of global stopping criteria for multi-objective optimization. In this study we propose a global stopping criterion, which is terms as MGBM after the authors surnames. MGBM combines a novel progress indicator, called mutual domination rate (MDR) indicator, with a simplified Kalman filter, which is used for evidence-gathering purposes. The MDR indicator, which is also introduced, is a special-purpose progress indicator designed for the purpose of stopping a multi-objective optimization. As part of the paper we describe the criterion from a theoretical perspective and examine its performance on a number of test problems. We also compare this method with similar approaches to the issue. The results of these experiments suggest that MGBM is a valid and accurate approach.},
    Year = {2016}
    }
    Abstract This paper puts forward a comprehensive study of the design of global stopping criteria for multi-objective optimization. In this study we propose a global stopping criterion, which is terms as MGBM after the authors surnames. MGBM combines a novel progress indicator, called mutual domination rate (MDR) indicator, with a simplified Kalman filter, which is used for evidence-gathering purposes. The MDR indicator, which is also introduced, is a special-purpose progress indicator designed for the purpose of stopping a multi-objective optimization. As part of the paper we describe the criterion from a theoretical perspective and examine its performance on a number of test problems. We also compare this method with similar approaches to the issue. The results of these experiments suggest that MGBM is a valid and accurate approach.
  • L. Martí, N. Sanchez-Pi, J. M. Molina, and A. C. Bicharra Garcia, “On the Combination of Support Vector Machines and Segmentation Algorithms for Anomaly Detection: A Petroleum Industry Comparative Study,” Journal of Applied Logic, 2016. doi: 10.1016/j.jal.2016.11.015 bibtex abstract
    @article{marti-2017:svm,
    Author = {Luis Mart{\'i} and Nayat Sanchez-Pi and Molina, Jos{\'e} Manuel and Bicharra Garcia, Ana Cristina},
    Issn = {1570-8683},
    Journal = {Journal of Applied Logic},
    doi = {10.1016/j.jal.2016.11.015},
    Title = {On the Combination of Support Vector Machines and Segmentation Algorithms for Anomaly Detection: {A} Petroleum Industry Comparative Study},
    Year = {2016},
    abstract = {Information retrieval has been widely studied due to the growing amounts of textual information available electronically. Nowadays organizations and industries are facing the challenge of organizing, analyzing and extracting knowledge from masses of unstructured information for decision making process. The development of automatic methods to produce usable structured information from unstructured text sources is extremely valuable to them. Opposed to the traditional text classification methods that need a set of well-classified trained corpus to perform efficient classification; the ontology-based classifier benefits from the domain knowledge and provides more accuracy. In a previous work we proposed and evaluated an ontology-based heuristic algorithm for occupational health control process, particularly, for the case of automatic detection of accidents from unstructured texts. Our extended proposal is more domain dependent because it uses technical terms and contrast the relevance of these technical terms into the text, so the heuristic is more accurate. It divides the problem in subtasks such as: (i) text analysis, (ii) recognition and (iii) classification of failed occupational health control, resolving accidents as text analysis, recognition and classification of failed occupational health control, resolving accidents.}
    }
    Information retrieval has been widely studied due to the growing amounts of textual information available electronically. Nowadays organizations and industries are facing the challenge of organizing, analyzing and extracting knowledge from masses of unstructured information for decision making process. The development of automatic methods to produce usable structured information from unstructured text sources is extremely valuable to them. Opposed to the traditional text classification methods that need a set of well-classified trained corpus to perform efficient classification; the ontology-based classifier benefits from the domain knowledge and provides more accuracy. In a previous work we proposed and evaluated an ontology-based heuristic algorithm for occupational health control process, particularly, for the case of automatic detection of accidents from unstructured texts. Our extended proposal is more domain dependent because it uses technical terms and contrast the relevance of these technical terms into the text, so the heuristic is more accurate. It divides the problem in subtasks such as: (i) text analysis, (ii) recognition and (iii) classification of failed occupational health control, resolving accidents as text analysis, recognition and classification of failed occupational health control, resolving accidents.
  • N. Sanchez-Pi, L. Martí, J. M. Molina, and A. C. Bicharra Garcia, “Contextual Pattern Discovery in Ambient Intelligent Application,” International Journal of Imaging and Robotics (IJIR), vol. 15, iss. 4, pp. 165-178, 2015. bibtex
    @article{marti-2015:ijir,
    Author = {Sanchez-Pi, Nayat and Mart\'i, Luis and Molina, Jos{\'e} Manuel and Bicharra Garcia, Ana Cristina},
    Journal = {International Journal of Imaging and Robotics (IJIR)},
    Number = {4},
    Pages = {165--178},
    Title = {Contextual Pattern Discovery in Ambient Intelligent Application},
    Volume = {15},
    Year = {2015}}
  • L. Martí, N. Sanchez-Pi, J. M. Molina, and A. C. Bicharra Garcia, “Anomaly Detection Based on Sensor Data in Petroleum Industry Applications,” Sensors, vol. 15, iss. 2, pp. 2774-2797, 2015. doi: 10.3390/s150202774 bibtex abstract url
    @article{marti-2015:sensors,
    Abstract = {Anomaly detection is the problem of finding patterns in data that do not conform to an a priori expected behavior. This is related to the problem in which some samples are distant, in terms of a given metric, from the rest of the dataset, where these anomalous samples are indicated as outliers. Anomaly detection has recently attracted the attention of the research community, because of its relevance in real-world applications, like intrusion detection, fraud detection, fault detection and system health monitoring, among many others. Anomalies themselves can have a positive or negative nature, depending on their context and interpretation. However, in either case, it is important for decision makers to be able to detect them in order to take appropriate actions. The petroleum industry is one of the application contexts where these problems are present. The correct detection of such types of unusual information empowers the decision maker with the capacity to act on the system in order to correctly avoid, correct or react to the situations associated with them. In that application context, heavy extraction machines for pumping and generation operations, like turbomachines, are intensively monitored by hundreds of sensors each that send measurements with a high frequency for damage prevention. In this paper, we propose a combination of yet another segmentation algorithm (YASA), a novel fast and high quality segmentation algorithm, with a one-class support vector machine approach for efficient anomaly detection in turbomachines. The proposal is meant for dealing with the aforementioned task and to cope with the lack of labeled training data. As a result, we perform a series of empirical studies comparing our approach to other methods applied to benchmark problems and a real-life application related to oil platform turbomachinery anomaly detection.},
    Annotation = {JCR Impact Factor: 2.048 (2013), 2.457 (5-year).\\ Qualis: A2.},
    Author = {Mart\'{i}, Luis and Sanchez-Pi, Nayat and Molina, Jos\'{e} Manuel and Bicharra Garcia, Ana Cristina},
    Date-Added = {2015-01-29 15:18:08 +0000},
    Date-Modified = {2015-01-29 15:33:23 +0000},
    Doi = {10.3390/s150202774},
    File = {journals/cover-sensors.pdf,journals/sensors.pdf},
    Issn = {1424-8220},
    Journal = {Sensors},
    Number = {2},
    Pages = {2774--2797},
    Title = {Anomaly Detection Based on Sensor Data in Petroleum Industry Applications},
    Url = {http://www.mdpi.com/1424-8220/15/2/2774},
    Volume = {15},
    Year = {2015}}
    Anomaly detection is the problem of finding patterns in data that do not conform to an a priori expected behavior. This is related to the problem in which some samples are distant, in terms of a given metric, from the rest of the dataset, where these anomalous samples are indicated as outliers. Anomaly detection has recently attracted the attention of the research community, because of its relevance in real-world applications, like intrusion detection, fraud detection, fault detection and system health monitoring, among many others. Anomalies themselves can have a positive or negative nature, depending on their context and interpretation. However, in either case, it is important for decision makers to be able to detect them in order to take appropriate actions. The petroleum industry is one of the application contexts where these problems are present. The correct detection of such types of unusual information empowers the decision maker with the capacity to act on the system in order to correctly avoid, correct or react to the situations associated with them. In that application context, heavy extraction machines for pumping and generation operations, like turbomachines, are intensively monitored by hundreds of sensors each that send measurements with a high frequency for damage prevention. In this paper, we propose a combination of yet another segmentation algorithm (YASA), a novel fast and high quality segmentation algorithm, with a one-class support vector machine approach for efficient anomaly detection in turbomachines. The proposal is meant for dealing with the aforementioned task and to cope with the lack of labeled training data. As a result, we perform a series of empirical studies comparing our approach to other methods applied to benchmark problems and a real-life application related to oil platform turbomachinery anomaly detection.
  • L. Martí, J. García, A. Berlanga, and J. M. Molina, “Multi-Objective Optimization with an Adaptive Resonance Theory-based Estimation of Distribution Algorithm,” Annals of Mathematics and Artificial Intelligence, vol. 68, iss. 4, pp. 247-273, 2013. doi: 10.1007/s10472-012-9303-0 bibtex abstract
    @article{marti-2012:marteda-amai,
    Abstract = {The introduction of learning to the search mechanisms of optimization algorithms has been nominated as one of the viable approaches when dealing with complex optimization problems, in particular with multi-objective ones. One of the forms of carrying out this hybridization process is by using multi-objective optimization estimation of distribution algorithms (MOEDAs). However, it has been pointed out that current MOEDAs have an intrinsic shortcoming in their model-building algorithms that hamper their performance. In this work, we put forward the argument that error-based learning, the class of learning most commonly used in MOEDAs is responsible for current MOEDA underachievement. We present adaptive resonance theory (ART) as a suitable learning paradigm alternative and present a novel algorithm called multi-objective ART-based EDA (MARTEDA) that uses a Gaussian ART neural network for model-building and a hypervolume-based selector as described for the HypE algorithm. In order to assert the improvement obtained by combining two cutting-edge approaches to optimization an extensive set of experiments are carried out. These experiments also test the scalability of MARTEDA as the number of objective functions increases.},
    Acmid = {2560185},
    Address = {Hingham, MA, USA},
    Author = {Mart\'{i}, Luis and Garc\'{i}a, Jes\'{u}s and Berlanga, Antonio and Molina, Jos\'{e} Manuel},
    Doi = {10.1007/s10472-012-9303-0},
    Issn = {1012-2443},
    Journal = {Annals of Mathematics and Artificial Intelligence},
    Keywords = {65K10, 68T05, 68T20, Adaptive resonance theory, Estimation of distribution algorithms, Multi-objective optimization},
    Number = {4},
    Numpages = {27},
    Pages = {247--273},
    Publisher = {Kluwer Academic Publishers},
    Title = {Multi-Objective Optimization with an Adaptive Resonance Theory-based Estimation of Distribution Algorithm},
    Volume = {68},
    Year = {2013}}
    The introduction of learning to the search mechanisms of optimization algorithms has been nominated as one of the viable approaches when dealing with complex optimization problems, in particular with multi-objective ones. One of the forms of carrying out this hybridization process is by using multi-objective optimization estimation of distribution algorithms (MOEDAs). However, it has been pointed out that current MOEDAs have an intrinsic shortcoming in their model-building algorithms that hamper their performance. In this work, we put forward the argument that error-based learning, the class of learning most commonly used in MOEDAs is responsible for current MOEDA underachievement. We present adaptive resonance theory (ART) as a suitable learning paradigm alternative and present a novel algorithm called multi-objective ART-based EDA (MARTEDA) that uses a Gaussian ART neural network for model-building and a hypervolume-based selector as described for the HypE algorithm. In order to assert the improvement obtained by combining two cutting-edge approaches to optimization an extensive set of experiments are carried out. These experiments also test the scalability of MARTEDA as the number of objective functions increases.
  • L. Martí, J. García, A. Berlanga, C. Coello Coello, and J. M. Molina, “MB-GNG: Addressing Drawbacks in Multi-Objective Optimization Estimation of Distribution Algorithms,” Operations Research Letters, vol. 39, iss. 2, pp. 150-154, 2011. doi: 10.1016/j.orl.2011.01.002 bibtex abstract
    @article{marti-2011:mb-gng-orl,
    Abstract = {We examine the model-building issue related to multi-objective estimation of distribution algorithms (MOEDAs) and show that some of their, as yet overlooked, characteristics render most current \{MOEDAs\} unviable when addressing optimization problems with many objectives. We propose a novel model-building growing neural gas (MB-GNG) network that is specially devised for properly dealing with that issue and therefore yields a better performance. Experiments are conducted in order to show from an empirical point of view the advantages of the new algorithm.},
    Author = {Mart\'{i}, Luis and Garc\'{i}a, Jes\'{u}s and Berlanga, Antonio and Coello Coello, Carlos~A. and Molina, Jos\'{e} Manuel},
    Doi = {10.1016/j.orl.2011.01.002},
    Issn = {0167-6377},
    Journal = {Operations Research Letters},
    Number = {2},
    Pages = {150--154},
    Title = {{MB-GNG}: {A}ddressing Drawbacks in Multi-Objective Optimization Estimation of Distribution Algorithms},
    Volume = {39},
    Year = {2011}}
    We examine the model-building issue related to multi-objective estimation of distribution algorithms (MOEDAs) and show that some of their, as yet overlooked, characteristics render most current \{MOEDAs\} unviable when addressing optimization problems with many objectives. We propose a novel model-building growing neural gas (MB-GNG) network that is specially devised for properly dealing with that issue and therefore yields a better performance. Experiments are conducted in order to show from an empirical point of view the advantages of the new algorithm.

Books

  • J. M. Molina, F. Chamorro, A. Ledezma, J. Carbó, L. Martí, Ó. Pérez, and J. García, Guía Didáctica: Programación en Lenguajes Estructurados (Grado Superior), Madrid: McGraw–Hill, 2006. bibtex url
    @book{molina-et-al-2006:gdplegs,
    Address = {Madrid},
    Author = {Molina, Jos\'{e} Manuel and Chamorro, F\'{e}lix and Ledezma, Agapito and Carb\'{o}, Javier and Mart\'{i}, Luis and P\'{e}rez, \'{O}scar and Garc\'{i}a, Jes\'{u}s},
    Isbn = {8448148711},
    Publisher = {McGraw--Hill},
    Title = {Gu\'{i}a Did\'{a}ctica: {P}rogramaci\'{o}n en Lenguajes Estructurados (Grado Superior)},
    Url = {http://www.mcgraw-hill.es/html/8448148711.html},
    Year = {2006}}
  • J. M. Molina, F. Chamorro, A. Ledezma, J. Carbó, L. Martí, Ó. Pérez, and J. García, Guía Didáctica: Fundamentos de Programación (Grado Superior), Madrid: McGraw–Hill, 2006. bibtex url
    @book{molina-et-al-2006:gdfpgs,
    Address = {Madrid},
    Author = {Molina, Jos\'{e} Manuel and Chamorro, F\'{e}lix and Ledezma, Agapito and Carb\'{o}, Javier and Mart\'{i}, Luis and P\'{e}rez, \'{O}scar and Garc\'{i}a, Jes\'{u}s},
    Isbn = {8448148681},
    Publisher = {McGraw--Hill},
    Title = {Gu\'{i}a Did\'{a}ctica: {F}undamentos de Programaci\'{o}n (Grado Superior)},
    Url = {http://www.mcgraw-hill.es/html/8448148681.html},
    Year = {2006}}
  • J. M. Molina, F. Chamorro, A. Ledezma, J. Carbó, L. Martí, Ó. Pérez, and J. García, Fundamentos de Programación (Grado Superior), Madrid: McGraw–Hill, 2006. bibtex url
    @book{molina-et-al-2006:fpgs,
    Address = {Madrid},
    Author = {Molina, Jos\'{e} Manuel and Chamorro, F\'{e}lix and Ledezma, Agapito and Carb\'{o}, Javier and Mart\'{i}, Luis and P\'{e}rez, \'{O}scar and Garc\'{i}a, Jes\'{u}s},
    Isbn = {8448148681},
    Publisher = {McGraw--Hill},
    Title = {Fundamentos de Programaci\'{o}n (Grado Superior)},
    Url = {http://www.mcgraw-hill.es/html/8448148681.html},
    Year = {2006}}
  • J. M. Molina, F. Chamorro, A. Ledezma, J. Carbó, L. Martí, Ó. Pérez, and J. García, Programación en Lenguajes Estructurados (Grado Superior), Madrid: McGraw–Hill, 2006. bibtex url
    @book{molina-et-al-2006:plegs,
    Address = {Madrid},
    Author = {Molina, Jos\'{e} Manuel and Chamorro, F\'{e}lix and Ledezma, Agapito and Carb\'{o}, Javier and Mart\'{i}, Luis and P\'{e}rez, \'{O}scar and Garc\'{i}a, Jes\'{u}s},
    Isbn = {8448148703},
    Publisher = {McGraw--Hill},
    Title = {Programaci\'{o}n en Lenguajes Estructurados (Grado Superior)},
    Url = {http://www.mcgraw-hill.es/html/8448148703.html},
    Year = {2006}}

In Collections

  • N. Sanchez-Pi, L. Martí, J. M. Molina, and A. C. Bicharra Garcia, “Information Fusion for Improving Decision-Making in Big Data Applications,” in Resource Management for Big Data Platforms: Algorithms, Modelling, and High-Performance Computing Techniques, F. Pop, J. Ko{l}odziej, and B. Di Martino, Eds., Heidelberg: Springer International Publishing, 2016, pp. 171-188. doi: 10.1007/978-3-319-44881-7_9 bibtex
    @incollection{sanchez-2016:big-data,
    author = {Sanchez-Pi, Nayat and Mart\'i, Luis and Molina, Jos\'e Manuel and Bicharra Garcia, Ana Cristina},
    editor = {Pop, Florin and Ko{\l}odziej, Joanna and Di Martino, Beniamino},
    title = {Information Fusion for Improving Decision-Making in Big Data Applications},
    booktitle = {Resource Management for Big Data Platforms: Algorithms, Modelling, and High-Performance Computing Techniques},
    year = {2016},
    publisher = {Springer International Publishing},
    series = {Computer Communications and Networks},
    pages = {171--188},
    isbn = {978-3-319-44881-7},
    doi = {10.1007/978-3-319-44881-7_9},
    Address = {Heidelberg},
    }
  • L. Martí, J. García, A. Berlanga, and J. M. Molina, “Multi-Objective Optimization with an Adaptive Resonance Theory-Based Estimation of Distribution Algorithm: A Comparative Study,” in Learning and Intelligent Optimization, C. A. Coello Coello, Ed., Berlin/Heidelberg: Springer, 2011, vol. 6683, pp. 458-472. doi: 10.1007/978-3-642-25566-3_36 bibtex abstract
    @incollection{marti-2011:marteda-lion,
    Abstract = {The introduction of learning to the search mechanisms of optimization algorithms has been nominated as one of the viable approaches when dealing with complex optimization problems, in particular with multi-objective ones. One of the forms of carrying out this hybridization process is by using multi-objective optimization estimation of distribution algorithms (MOEDAs). However, it has been pointed out that current MOEDAs have a intrinsic shortcoming in their model-building algorithms that hamper their performance. In this work we argue that error-based learning, the class of learning most commonly used in MOEDAs is responsible for current MOEDA underachievement. We present adaptive resonance theory (ART) as a suitable learning paradigm alternative and present a novel algorithm called multi-objective ART-based EDA (MARTEDA) that uses a Gaussian ART neural network for model-building and an hypervolume-based selector as described for the HypE algorithm. In order to assert the improvement obtained by combining two cutting-edge approaches to optimization an extensive set of experiments are carried out. These experiments also test the scalability of MARTEDA as the number of objective functions increases.},
    Address = {Berlin/Heidelberg},
    Affiliation = {Group of Applied Artificial Intelligence, Universidad Carlos III de Madrid, Av. de la Universidad Carlos III, 22. Colmenarejo, Madrid, 28270 Spain},
    Author = {Mart\'i, Luis and Garc\'ia, Jes{\'u}s and Berlanga, Antonio and Molina, Jos\'e Manuel},
    Booktitle = {Learning and Intelligent Optimization},
    Doi = {10.1007/978-3-642-25566-3_36},
    Editor = {Coello Coello, Carlos A.},
    Isbn = {978-3-642-25565-6},
    Keyword = {Computer Science},
    Pages = {458-472},
    Publisher = {Springer},
    Series = {Lecture Notes in Computer Science},
    Title = {Multi-Objective Optimization with an Adaptive Resonance Theory-Based Estimation of Distribution Algorithm: {A} Comparative Study},
    Volume = {6683},
    Year = {2011}}
    The introduction of learning to the search mechanisms of optimization algorithms has been nominated as one of the viable approaches when dealing with complex optimization problems, in particular with multi-objective ones. One of the forms of carrying out this hybridization process is by using multi-objective optimization estimation of distribution algorithms (MOEDAs). However, it has been pointed out that current MOEDAs have a intrinsic shortcoming in their model-building algorithms that hamper their performance. In this work we argue that error-based learning, the class of learning most commonly used in MOEDAs is responsible for current MOEDA underachievement. We present adaptive resonance theory (ART) as a suitable learning paradigm alternative and present a novel algorithm called multi-objective ART-based EDA (MARTEDA) that uses a Gaussian ART neural network for model-building and an hypervolume-based selector as described for the HypE algorithm. In order to assert the improvement obtained by combining two cutting-edge approaches to optimization an extensive set of experiments are carried out. These experiments also test the scalability of MARTEDA as the number of objective functions increases.
  • M. J. Pérez, J. García, L. Martí, and J. M. Molina, “Multi-objective Optimization Evolutionary Algorithms in Insurance Linked Derivatives,” in Handbook of Research on Nature-inspired Computing for Economics and Management, J. -P. Rennard, Ed., London: Idea Group, 2006, vol. II, pp. 885-908. doi: 10.4018/978-1-59140-984-7.ch057 bibtex abstract url
    @incollection{perez-et-al-2006:moea-in-insurance,
    Abstract = {This work addresses a real-world adjustment of economic models where the application of robust and global optimization techniques is required. The problem dealt with is the search for a set of parameters to calculate the reported claim amount. Several functions are proposed to obtain the reported claim amount, and a multi-objective optimization procedure is used to obtain parameters using real data and to decide the best function to approximate the reported claim amount. Using this function, insurance companies negotiate the underlying contract-that is, the catastrophic loss ratio defined from the total reported claim amount. They are associated with catastrophes that occurred during the loss period and declared until the development period expired. The suitability of different techniques coming from evolutionary computation (EC) to solve this problem is explored, contrasting the performance achieved with recent proposals of multi-objective evolutionary algorithms (MOEAs). Results show the advantages of MOEAs in the proposal in terms of effectiveness and completeness in searching for solutions, compared with particular solutions of classical EC approaches (using an aggregation operator) in problems with real data.},
    Address = {London},
    Author = {P\'{e}rez, Mar\'{i}a Jos\'{e} and Garc\'{i}a, Jes\'{u}s and Mart\'{i}, Luis and Molina, Jos\'{e} Manuel},
    Booktitle = {Handbook of Research on Nature-inspired Computing for Economics and Management},
    Date-Modified = {2010-12-23 00:06:23 +0100},
    Doi = {10.4018/978-1-59140-984-7.ch057},
    Editor = {Rennard, J.-P.},
    Pages = {885--908},
    Publisher = {Idea Group},
    Title = {Multi-objective Optimization Evolutionary Algorithms in Insurance Linked Derivatives},
    Url = {http://www.igi-global.com/bookstore/Chapter.aspx?TitleId=21172},
    Volume = {II},
    Year = {2006}}
    This work addresses a real-world adjustment of economic models where the application of robust and global optimization techniques is required. The problem dealt with is the search for a set of parameters to calculate the reported claim amount. Several functions are proposed to obtain the reported claim amount, and a multi-objective optimization procedure is used to obtain parameters using real data and to decide the best function to approximate the reported claim amount. Using this function, insurance companies negotiate the underlying contract-that is, the catastrophic loss ratio defined from the total reported claim amount. They are associated with catastrophes that occurred during the loss period and declared until the development period expired. The suitability of different techniques coming from evolutionary computation (EC) to solve this problem is explored, contrasting the performance achieved with recent proposals of multi-objective evolutionary algorithms (MOEAs). Results show the advantages of MOEAs in the proposal in terms of effectiveness and completeness in searching for solutions, compared with particular solutions of classical EC approaches (using an aggregation operator) in problems with real data.
  • L. Martí, A. Policriti, and L. García, “Modelos Neurodifusos Híbridos Basados en la Teoría de Resonancia Adaptativa,” in Optimización Inteligente, G. Joya, M. ~A. Atencia, A. Ochoa, and S. Allende, Eds., Málaga: Servicio de Publicaciones de la Universidad de Málaga (SPICUM), 2003, pp. 363-412. bibtex url
    @incollection{marti-2003:malaga-book,
    Address = {M\'alaga},
    Author = {Mart\'i, Luis and Policriti, Alberto and Garc\'ia, Luciano},
    Booktitle = {Optimizaci\'on Inteligente},
    Editor = {Joya, Gonzalo and Atencia, M.~A. and Ochoa, Alberto and Allende, Sira},
    Isbn = {84-9747-034-6},
    Pages = {363--412},
    Publisher = {Servicio de Publicaciones de la Universidad de M\'alaga (SPICUM)},
    Title = {Modelos Neurodifusos H\'{i}bridos Basados en la Teor\'{i}a de Resonancia Adaptativa},
    Url = {http://malaka.spicum.uma.es/libro.php?idLibro=909},
    Year = {2003},
    Bdsk-Url-1 = {http://malaka.spicum.uma.es/libro.php?idLibro=909}}
  • L. Martí, A. Policriti, and L. García, “Hybrid Adaptive Resonance Theory neural networks for function approximation,” in Innovations in Intelligent Systems and Applications: Design, Management and Applications, A. Abraham, L. C. Jain, and B. J. van der Zwaag, Eds., Heidelberg: Physica–Verlag (Springer), 2003, pp. 51-88. bibtex url
    @incollection{marti-book-chapter,
    Address = {Heidelberg},
    Author = {Mart\'i, Luis and Policriti, Alberto and Garc\'ia, Luciano},
    Booktitle = {Innovations in Intelligent Systems and Applications: Design, Management and Applications},
    Editor = {Abraham, Ajith and Jain, Lakmi C. and van der Zwaag, Berend Jan},
    Isbn = {978-3-540-20265-3},
    Pages = {51--88},
    Publisher = {Physica--Verlag (Springer)},
    Series = {Studies in Fuzziness and Soft Computing},
    Title = {Hybrid Adaptive Resonance Theory neural networks for function approximation},
    Url = {http://www.springer.com/engineering/book/978-3-540-20265-3},
    Year = {2003}}

In Proceedings

  • L. Martí, A. Fansi-Tchango, L. Navarro, and M. Schoenauer, “Progressively Adding Objectives: A Case Study in Anomaly Detection,” in Proceedings of the 2017 Annual Conference on Genetic and Evolutionary Computation (GECCO’17), New York, NY, USA, 2017, pp. 1-8. doi: 10.1145/3071178.3071333 bibtex abstract
    @inproceedings{marti-2017:gecco,
    author = {Luis Mart\'i and Arsene Fansi-Tchango and Laurent Navarro and Marc Schoenauer},
    title = {Progressively Adding Objectives: {A} Case Study in Anomaly Detection},
    booktitle = {Proceedings of the 2017 Annual Conference on Genetic and Evolutionary Computation (GECCO'17)},
    year = {2017},
    doi = {10.1145/3071178.3071333},
    isbn = {978-1-4503-4920},
    note = {in press},
    publisher = {ACM Press},
    location = {Berlin, Germany},
    pages = {1--8},
    address = {New York, NY, USA},
    keywords = {anomaly detection, artificial immune systems, multi-objective optimization, voronoi diagrams},
    abstract = {One of the principles of evolutionary multi-objective optimization is the conjoint optimization of the objective functions. However, in some cases, some of the objectives are easier to attain than others. This causes the population to lose diversity at a high rate and stagnate in early stages of the evolution. This paper presents the progressive addition of objectives (PAO) heuristic. PAO gradually adds objectives to a given problem relying on a perceived measure of complexity. This diversity loss phenomenon caused by the nature of a given objective has been observed when applying the Voronoi diagram-based evolutionary algorithm (VorEAl) in anomaly detection problems. Consequently, PAO has been first directed to address that issue. The experimental studies carried out show that the PAO heuristic manages to yield better results than the direct use of VorEAl on a group of test problems.}
    }
    One of the principles of evolutionary multi-objective optimization is the conjoint optimization of the objective functions. However, in some cases, some of the objectives are easier to attain than others. This causes the population to lose diversity at a high rate and stagnate in early stages of the evolution. This paper presents the progressive addition of objectives (PAO) heuristic. PAO gradually adds objectives to a given problem relying on a perceived measure of complexity. This diversity loss phenomenon caused by the nature of a given objective has been observed when applying the Voronoi diagram-based evolutionary algorithm (VorEAl) in anomaly detection problems. Consequently, PAO has been first directed to address that issue. The experimental studies carried out show that the PAO heuristic manages to yield better results than the direct use of VorEAl on a group of test problems.
  • L. Martí, H. Dias de Mello Jr, N. Sanchez-Pi, and M. Rebuzzi Vellasco, “SMS-EDA-MEC: Extending Copula-based EDAs to Multi-Objective Optimization,” in Proceedings of the 2016 IEEE Congress on Evolutionary Computation (IEEE CEC 2016) part of the 2016 IEEE World Congress on Computational Intelligence (IEEE WCCI 2016), 2016, pp. 3726-3733. doi: 10.1109/CEC.2016.7744261 bibtex abstract
    @inproceedings{marti-2016:cec,
    author = {Luis Mart\'i and Dias de Mello Jr, Harold and Nayat Sanchez-Pi and Rebuzzi Vellasco, Marley~M.~B.},
    title = {{SMS-EDA-MEC}: {E}xtending Copula-based {EDAs} to Multi-Objective Optimization},
    booktitle = {Proceedings of the 2016 IEEE Congress on Evolutionary Computation (IEEE CEC 2016) part of the 2016 IEEE World Congress on Computational Intelligence (IEEE WCCI 2016)},
    year = {2016},
    publisher = {IEEE Press},
    month = {7},
    location = {Vancouver, Canada},
    pages={3726-3733},
    abstract={It can be argued that in order to produce a sub-stantial improvement in multi-objective estimation of distribution algorithms it is necessary to focus on a particular group of issues, in particular, on the weaknesses derived from multi-objective fitness assignment and selection methods, the incorrect treatment of relevant but isolated (precursor) individuals; the loss of population diversity, and the use of `general purpose' modeling algorithms without taking note of the particular requirements of the task. In this work we introduce the S-Metric Selection Estimation of Distribution Algorithm based on Multivariate Extension of Copulas (SMS-EDA-MEC). SMS-EDA-MEC was devised with the intention of dealing with those issues in mind. It builds the population model relying on the comprehensive Clayton's copula and incorporates methods for automatic population restarting and for priming precursor individuals. The experimental studies presented show that SMS-EDA-MEC yields better results than current and `traditional' approaches.},
    keywords={evolutionary computation;stochastic programming;S-metric selection;SMS-EDA-MEC;copula-based EDA;estimation of distribution algorithm;general purpose modeling algorithms;multiobjective fitness assignment;multiobjective optimization;multivariate extension of copulas;population diversity loss;Estimation;Evolutionary computation;Generators;Optimization;Proposals;Sociology;Statistics},
    doi={10.1109/CEC.2016.7744261},
    }
    It can be argued that in order to produce a sub-stantial improvement in multi-objective estimation of distribution algorithms it is necessary to focus on a particular group of issues, in particular, on the weaknesses derived from multi-objective fitness assignment and selection methods, the incorrect treatment of relevant but isolated (precursor) individuals; the loss of population diversity, and the use of `general purpose’ modeling algorithms without taking note of the particular requirements of the task. In this work we introduce the S-Metric Selection Estimation of Distribution Algorithm based on Multivariate Extension of Copulas (SMS-EDA-MEC). SMS-EDA-MEC was devised with the intention of dealing with those issues in mind. It builds the population model relying on the comprehensive Clayton’s copula and incorporates methods for automatic population restarting and for priming precursor individuals. The experimental studies presented show that SMS-EDA-MEC yields better results than current and `traditional’ approaches.
  • D. Cinalli, L. Martí, N. Sanchez-Pi, and A. C. Bicharra Garcia, “Bio-Inspired Algorithms and Preferences for Multi-objective Problems,” in 11th International Conference Hybrid Artificial Intelligent Systems (HAIS 2016), 2016, pp. 238-249. doi: 10.1007/978-3-319-32034-2_20 bibtex
    @inproceedings{Cinalli2016,
    Author = {Cinalli, Daniel and Mart\'i, Luis and Sanchez-Pi, Nayat and Bicharra Garcia, Ana Cristina},
    Booktitle = {11th International Conference Hybrid Artificial Intelligent Systems (HAIS 2016)},
    Doi = {10.1007/978-3-319-32034-2_20},
    Editor = {Mart\'inez-\'Alvarez, Francisco and Troncoso, Alicia and Quinti{\'a}n, H{\'e}ctor and Corchado, Emilio},
    Isbn = {978-3-319-32034-2},
    Location = {Sevilla, Spain},
    Pages = {238--249},
    Publisher = {Springer International Publishing},
    Title = {Bio-Inspired Algorithms and Preferences for Multi-objective Problems},
    Year = {2016}}
  • L. Martí, A. Fansi-Tchango, L. Navarro, and M. Schoenauer, “VorAIS: A Multi-Objective Voronoi Diagram-based Artificial Immune System,” in Proceedings of the 2016 Annual Conference on Genetic and Evolutionary Computation (GECCO’16), New York, NY, USA, 2016, pp. 11-12. doi: 10.1145/2908961.2909027 bibtex abstract
    @inproceedings{marti-2016:gecco,
    author = {Luis Mart\'i and Arsene Fansi-Tchango and Laurent Navarro and Marc Schoenauer},
    title = {{VorAIS}: {A} Multi-Objective {V}oronoi Diagram-based Artificial Immune System},
    booktitle = {Proceedings of the 2016 Annual Conference on Genetic and Evolutionary Computation (GECCO'16)},
    year = {2016},
    doi = {10.1145/2908961.2909027},
    isbn = {978-1-4503-4323-7},
    publisher = {ACM Press},
    location = {Denver (CO) USA},
    pages = {11--12},
    address = {New York, NY, USA},
    keywords = {anomaly detection, artificial immune systems, multi-objectie optimization, voronoi diagrams},
    abstract = {This paper introduces the Voronoi diagram-based Artificial Immune System (VorAIS). VorAIS models the self/non-self using a Voronoi diagram that determines which areas of the problem domain correspond to self or to non-self. The diagram is evolved using a multi-objective bio-inspired approach in order to conjointly optimize various classification metrics (accuracy, recall and specificity). VorAIS is experimentally validated, first on standard classification problems, then on the well-known NSL-KDD dataset for anomaly detection where it favorably compares with other AIS approaches.}
    }
    This paper introduces the Voronoi diagram-based Artificial Immune System (VorAIS). VorAIS models the self/non-self using a Voronoi diagram that determines which areas of the problem domain correspond to self or to non-self. The diagram is evolved using a multi-objective bio-inspired approach in order to conjointly optimize various classification metrics (accuracy, recall and specificity). VorAIS is experimentally validated, first on standard classification problems, then on the well-known NSL-KDD dataset for anomaly detection where it favorably compares with other AIS approaches.
  • J. Jiménez Alemán, N. Sanchez-Pi, L. Martí, J. M. Molina, and A. C. Bicharra Garcia, “A Data Fusion Model for Ambient Assisted Living,” in Highlights of Practical Applications of Scalable Multi-Agent Systems. The PAAMS Collection: International Workshops of PAAMS 2016 Proceedings, 2016, pp. 301-312. doi: 10.1007/978-3-319-39387-2_25 bibtex
    @inproceedings{aleman2016:paams,
    Author = {Jim\'{e}nez Alem\'{a}n, Javier and Sanchez-Pi, Nayat and Mart\'{i}, Luis and Molina, Jos\'{e} Manuel and Bicharra Garcia, Ana Cristina},
    Booktitle = {Highlights of Practical Applications of Scalable Multi-Agent Systems. The PAAMS Collection: International Workshops of PAAMS 2016 Proceedings},
    Doi = {10.1007/978-3-319-39387-2_25},
    Editor = {Bajo, Javier and Escalona, Jos\'{e} Mar\'{i}a and Giroux, Sylvain and Hoffa-D{\k{a}}browska, Patrycja and Juli{\'a}n, Vicente and Novais, Paulo and Sanchez-Pi, Nayat and Unland, Rainer and Azambuja-Silveira, Ricardo},
    Isbn = {978-3-319-39387-2},
    Location = {Sevilla, Spain},
    Pages = {301--312},
    Publisher = {Springer International Publishing},
    Title = {A Data Fusion Model for Ambient Assisted Living},
    Year = {2016}
    }
  • L. Martí, A. Fansi-Tchango, L. Navarro, and M. Schoenauer, “Anomaly Detection with the Voronoi Diagram Evolutionary Algorithm,” in Proceedings of the 14th International Conference Parallel Problem Solving from Nature (PPSN XIV), 2016, pp. 697-706. doi: 10.1007/978-3-319-45823-6_65 bibtex abstract
    @inproceedings{marti-2016:ppsn,
    author={Mart\'i, Luis and Fansi-Tchango, Arsene and Navarro, Laurent and Schoenauer, Marc},
    editor={Handl, Julia and Hart, Emma and Lewis, R. Peter and L\'opez-Ib\'a\~{n}ez, Manuel and Ochoa, Gabriela and Paechter, Ben},
    title={Anomaly Detection with the {V}oronoi Diagram Evolutionary Algorithm},
    booktitle={Proceedings of the 14th International Conference Parallel Problem Solving from Nature (PPSN XIV)},
    year={2016},
    month={9},
    publisher={Springer International Publishing},
    location = {Edinburgh, UK},
    pages={697--706},
    isbn={978-3-319-45823-6},
    doi={10.1007/978-3-319-45823-6_65},
    abstract = {This paper presents the Voronoi diagram-based evolutionary algorithm (VorEAl).
    VorEAl partitions input space in abnormal/normal subsets using Voronoi diagrams.
    Diagrams are evolved using a multi-objective bio-inspired approach in order to conjointly
    optimize classification metrics while also being able to represent areas of the data space
    that are not present in the training dataset. As part of the paper, VorEAl is experimentally
    validated and contrasted with similar approaches.}
    }
    This paper presents the Voronoi diagram-based evolutionary algorithm (VorEAl). VorEAl partitions input space in abnormal/normal subsets using Voronoi diagrams. Diagrams are evolved using a multi-objective bio-inspired approach in order to conjointly optimize classification metrics while also being able to represent areas of the data space that are not present in the training dataset. As part of the paper, VorEAl is experimentally validated and contrasted with similar approaches.
  • W. Marinho and L. Martí, “Review of Student Proficiency Modeling Techniques for use in Intelligent Tutoring Systems,” in Workshop de Pesquisa e Desenvolvimento em Inteligêcia Artificial, Inteligêcia Collectiva e Ciêcia de Dados, 2016. bibtex url
    @inproceedings{marinho-2016:workpedia,
    title = {Review of Student Proficiency Modeling Techniques for use in Intelligent Tutoring Systems},
    author = {Wemerson Marinho and Luis Mart\'i},
    year = {2016},
    booktitle ={Workshop de Pesquisa e Desenvolvimento em Intelig\^{e}cia Artificial, Intelig\^{e}cia Collectiva e Ci\^{e}cia de Dados},
    url = {http://www.addlabs.uff.br/workpedia2016/anais-do-workpedia-2016/}
    }
  • D. Cinalli, L. Martí, N. Sanchez-Pi, and A. C. Bicharra Garcia, “Collective Preferences in Evolutionary Multi-objective Optimization: Techniques and Potential Contributions of Collective Intelligence,” in Proceedings of the 30th Annual ACM Symposium on Applied Computing, New York, NY, USA, 2015, pp. 133-138. doi: 10.1145/2695664.2695926 bibtex abstract
    @inproceedings{cinalli-2015:sac,
    Abstract = {This paper reviews suitable techniques of interactive and preference-based evolutionary multi-objective algorithms to achieve feasible solutions in Pareto-optimal front. We discuss about possible advantages of collective environments to aggregate consistent preferences in the optimization process. Decision maker can highlight the regions of Pareto frontier that are more relevant to him and focus the search only on those areas previously selected. In addition, interactive and cooperative genetic algorithms work on refining users' preferences throughout the optimization process to improve the reference point or fitness function. Nevertheless, expressing preferences from a unique or small group of decision makers may raise unilateral choices issues and pour hints in terms of search parameter. Supported by a large group of human interaction, collective intelligence is suggested to enhance multi-objective results and explore a wider variety of answers.},
    Acmid = {2695926},
    Address = {New York, NY, USA},
    Author = {Cinalli, Daniel and Mart\'i, Luis and Sanchez-Pi, Nayat and Bicharra Garcia, Ana Cristina},
    Booktitle = {Proceedings of the 30th Annual ACM Symposium on Applied Computing},
    Doi = {10.1145/2695664.2695926},
    Isbn = {978-1-4503-3196-8},
    Keywords = {collective intelligence, evolutionary multi-objective optimization algorithms, preferences, reference points},
    Location = {Salamanca, Spain},
    Numpages = {6},
    Pages = {133--138},
    Publisher = {ACM Press},
    Series = {SAC'15},
    Title = {Collective Preferences in Evolutionary Multi-objective Optimization: {T}echniques and Potential Contributions of Collective Intelligence},
    Year = {2015}}
    This paper reviews suitable techniques of interactive and preference-based evolutionary multi-objective algorithms to achieve feasible solutions in Pareto-optimal front. We discuss about possible advantages of collective environments to aggregate consistent preferences in the optimization process. Decision maker can highlight the regions of Pareto frontier that are more relevant to him and focus the search only on those areas previously selected. In addition, interactive and cooperative genetic algorithms work on refining users’ preferences throughout the optimization process to improve the reference point or fitness function. Nevertheless, expressing preferences from a unique or small group of decision makers may raise unilateral choices issues and pour hints in terms of search parameter. Supported by a large group of human interaction, collective intelligence is suggested to enhance multi-objective results and explore a wider variety of answers.
  • D. Cinalli, L. Martí, N. Sanchez-Pi, and A. C. Bicharra Garcia, “Collaborative Preferences in Multi-Objective Evolutionary Algorithms,” in II Simpósio Brasileiro de Sistemas Colaborativos (SBSC 2015), 2015. bibtex
    @inproceedings{cinalli-2015:sbsc,
    Author = {Cinalli, Daniel and Mart\'i, Luis and Sanchez-Pi, Nayat and Bicharra Garcia, Ana Cristina},
    booktitle = {II Simp\'osio Brasileiro de Sistemas Colaborativos (SBSC 2015)},
    title = {Collaborative Preferences in Multi-Objective Evolutionary Algorithms},
    year = {2015},
    location = {Salvador (BA) Brazil}
    }
  • D. Trevisan, N. Sanchez-Pi, L. Martí, and A. C. Bicharra Garcia, “Big Data Visualization for Occupational Health and Security Problem in Oil and Gas Industry,” in Human Interface and the Management of Information. Information and Knowledge Design, 2015, pp. 46-54. doi: 10.1007/978-3-319-20612-7_5 bibtex abstract
    @inproceedings{trevisan-2015:hcii,
    Abstract = {Association rule learning is a popular and well-researched set of methods for discovering interesting relations between entities in large databases in real-world problems. In this regard, an intelligent offshore oil industry environment is a very complex scenario and Occupational Health and Security (OHS) is a priority issue as it is an important factor to reduce the number of accidents and incidents records. In the oil industry, there exist standards to identify and record workplace accidents and incidents in order to provide guiding means on prevention efforts, indicating specific failures or reference, means of correction of conditions or circumstances that culminated in accident. OHS's employees are in charge of analyzing the mined rules to extract knowledge. In most of cases these users has two main challenges during this process: (i) to explore the measures of interestingness (confidence, lift, support, etc.) and (ii) to understand and analyze the large number of association rules. In this sense, an intuitive visualization of mined rules becomes a key component in a decision-making process. In this paper, we propose a novel visualization of spatio-temporal rules that provides the big picture about risk analysis in a real world environment. Our main contribution lies in an interactive visualization of accident interpretations by means of well-defined spatio-temporal constraints, in the oil industry domain.},
    Author = {Trevisan, Daniela and Sanchez-Pi, Nayat and Mart\'{i}, Luis and Bicharra Garcia, Ana Cristina},
    Booktitle = {Human Interface and the Management of Information. Information and Knowledge Design},
    Doi = {10.1007/978-3-319-20612-7_5},
    Editor = {Yamamoto, Sakae},
    Isbn = {978-3-319-20611-0},
    Keywords = {Data visualization; Big data applications; Decision support systems; Oil and gas industry},
    Pages = {46--54},
    Publisher = {Springer International Publishing},
    Series = {Lecture Notes in Computer Science},
    Title = {Big Data Visualization for Occupational Health and Security Problem in Oil and Gas Industry},
    Volume = {9172},
    Year = {2015}}
    Association rule learning is a popular and well-researched set of methods for discovering interesting relations between entities in large databases in real-world problems. In this regard, an intelligent offshore oil industry environment is a very complex scenario and Occupational Health and Security (OHS) is a priority issue as it is an important factor to reduce the number of accidents and incidents records. In the oil industry, there exist standards to identify and record workplace accidents and incidents in order to provide guiding means on prevention efforts, indicating specific failures or reference, means of correction of conditions or circumstances that culminated in accident. OHS’s employees are in charge of analyzing the mined rules to extract knowledge. In most of cases these users has two main challenges during this process: (i) to explore the measures of interestingness (confidence, lift, support, etc.) and (ii) to understand and analyze the large number of association rules. In this sense, an intuitive visualization of mined rules becomes a key component in a decision-making process. In this paper, we propose a novel visualization of spatio-temporal rules that provides the big picture about risk analysis in a real world environment. Our main contribution lies in an interactive visualization of accident interpretations by means of well-defined spatio-temporal constraints, in the oil industry domain.
  • N. Sanchez-Pi, M. Luis, J. M. Molina, and A. C. Bicharra Garcia, “Ontology Definition and Cognitive Analysis in Ocupational Health and Security (OHS) Environments,” in Proceedings of the 30th Annual ACM Symposium on Applied Computing, New York, NY, USA, 2015, pp. 201-206. doi: 10.1145/2695664.2695891 bibtex abstract
    @inproceedings{sanchez-2015:sac,
    Abstract = {Events recognition is central to occupational health and safety OHS, since the system can selectively start proper prediction services according to the user current situation and past knowledge taken from huge databases. In this sense, a fusion framework that combines data from multiples sources to achieve more specific inferences is needed. Our proposed model provides the big picture about risk analysis for that employee at that place in that moment in a real world environment. Our main contribution lies in building a causality model for accidents investigation by means of a well-defined spatiotemporal constraints on offshore oil industry domain.},
    Acmid = {2695891},
    Address = {New York, NY, USA},
    Author = {Sanchez-Pi, Nayat and Mart\'i Luis and Molina, Jos\'{e} Manuel and Bicharra Garcia, Ana Cristina},
    Booktitle = {Proceedings of the 30th Annual ACM Symposium on Applied Computing},
    Doi = {10.1145/2695664.2695891},
    Isbn = {978-1-4503-3196-8},
    Keywords = {data fusion, oil industry, ontology},
    Location = {Salamanca, Spain},
    Numpages = {6},
    Pages = {201--206},
    Publisher = {ACM Press},
    Series = {SAC'15},
    Title = {Ontology Definition and Cognitive Analysis in Ocupational Health and Security ({OHS}) Environments},
    Year = {2015}}
    Events recognition is central to occupational health and safety OHS, since the system can selectively start proper prediction services according to the user current situation and past knowledge taken from huge databases. In this sense, a fusion framework that combines data from multiples sources to achieve more specific inferences is needed. Our proposed model provides the big picture about risk analysis for that employee at that place in that moment in a real world environment. Our main contribution lies in building a causality model for accidents investigation by means of a well-defined spatiotemporal constraints on offshore oil industry domain.
  • D. Cinalli, L. Martí, N. Sanchez-Pi, and A. C. Bicharra Garcia, “Using Collective Intelligence to Support Multi-objective Decisions: Collaborative and Online Preferences,” in 30th IEEE/ACM International Conference on Automated Software Engineering Workshop (ASEW), 2015, pp. 82-85. doi: 10.1109/ASEW.2015.12 bibtex abstract
    @inproceedings{cinalli-2015:asew,
    Abstract = {This research indicates a novel approach of evolutionary multi-objective optimization algorithms meant for integrating collective intelligence methods into the optimization process. The new algorithms allow groups of decision makers to improve the successive stages of evolution via users' preferences and collaboration in a direct crowdsourcing fashion. They can, also, highlight the regions of Pareto frontier that are more relevant to the group of decision makers as to focus the search process mainly on those areas. As part of this work we test the algorithms performance when face with some synthetic problem as well as a real-world case scenario.},
    Author = {Daniel Cinalli and Luis Mart\'{i} and Nayat Sanchez-Pi and Bicharra Garcia, Ana Cristina},
    Booktitle = {30th IEEE/ACM International Conference on Automated Software Engineering Workshop (ASEW)},
    Doi = {10.1109/ASEW.2015.12},
    Isbn = {978-1-4673-9775-9},
    Location = {Lincoln (NE) USA},
    Pages = {82--85},
    Title = {Using Collective Intelligence to Support Multi-objective Decisions: {C}ollaborative and Online Preferences},
    Year = {2015}}
    This research indicates a novel approach of evolutionary multi-objective optimization algorithms meant for integrating collective intelligence methods into the optimization process. The new algorithms allow groups of decision makers to improve the successive stages of evolution via users’ preferences and collaboration in a direct crowdsourcing fashion. They can, also, highlight the regions of Pareto frontier that are more relevant to the group of decision makers as to focus the search process mainly on those areas. As part of this work we test the algorithms performance when face with some synthetic problem as well as a real-world case scenario.
  • D. Cinalli, L. Martí, N. Sanchez-Pi, and A. C. Garcia Bicharra Garcia, “Integrating collective intelligence into evolutionary multi-objective algorithms: Interactive preferences,” in 2015 IEEE Latin America Congress on Computational Intelligence (LA-CCI), 2015, pp. 1-6. doi: 10.1109/LA-CCI.2015.7435952 bibtex
    @inproceedings{cinalli2015integrating,
    Author = {Cinalli, Daniel and Mart\'i, Luis and Sanchez-Pi, Nayat and Garcia, Bicharra Garcia, Ana Cristina},
    Booktitle = {2015 IEEE Latin America Congress on Computational Intelligence (LA-CCI)},
    Doi = {10.1109/LA-CCI.2015.7435952},
    Organization = {IEEE},
    Pages = {1--6},
    Title = {Integrating collective intelligence into evolutionary multi-objective algorithms: {I}nteractive preferences},
    Year = {2015}}
  • L. Martí, C. Grimme, P. Kerschke, H. Trautmann, and G. Rudolph, “Averaged Hausdorff Approximations of Pareto Fronts Based on Multiobjective Estimation of Distribution Algorithms,” in Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation, New York, NY, USA, 2015, pp. 1427-1428. doi: 10.1145/2739482.2764631 bibtex abstract
    @inproceedings{marti-2015:hausdorff,
    Abstract = {We propose a post-processing strategy which consists of applying the averaged Hausdorff indicator to the complete archive of solutions after optimization by multiobjective estimation of distribution algorithms (MEDAs) to select a uniformly distributed subset of non-dominated solutions.},
    Acmid = {2764631},
    Address = {New York, NY, USA},
    Author = {Mart\'i, Luis and Grimme, Christian and Kerschke, Paskal and Trautmann, Heike and Rudolph, G\"{u}nter},
    Booktitle = {Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation},
    Doi = {10.1145/2739482.2764631},
    Editor = {Jim\'{e}nez Laredo, Juan Luis and Sara Silva and Esparcia{-}Alc\'{a}zar, Anna Isabel},
    Isbn = {978-1-4503-3488-4},
    Keywords = {averaged hausdorff distance, estimation of distribution algorithm, multiobjective optimization},
    Location = {Madrid, Spain},
    Pages = {1427--1428},
    Publisher = {ACM Press},
    Series = {GECCO Companion '15},
    Title = {Averaged {H}ausdorff Approximations of {P}areto Fronts Based on Multiobjective Estimation of Distribution Algorithms},
    Year = {2015}}
    We propose a post-processing strategy which consists of applying the averaged Hausdorff indicator to the complete archive of solutions after optimization by multiobjective estimation of distribution algorithms (MEDAs) to select a uniformly distributed subset of non-dominated solutions.
  • D. Brockhoff, M. Ehrgott, J. R. Figueira, L. Martí, L. Paquete, M. Stiglmayr, and D. Vanderpooten, “Computational Complexity (WG2),” in Understanding Complexity in Multiobjective Optimization — Report from Dagstuhl Seminar 15031, Dagstuhl, Germany, 2015, pp. 116-121. bibtex url
    @inproceedings{brockhoff2015computational,
    title = {Computational Complexity ({WG2})},
    author = {Brockhoff, Dimo and Ehrgott, Matthias and Figueira, Jos\'e Rui and Mart\'i, Luis and Paquete, Lu\'is and Stiglmayr, Michael and Vanderpooten, Daniel},
    booktitle = {Understanding Complexity in Multiobjective Optimization --- Report from Dagstuhl Seminar 15031},
    editor = {Salvatore Greco and Kathrin Klamroth and Joshua D. Knowles and G\"unter Rudolph},
    pages = {116--121},
    year = {2015},
    publisher = {Schloss Dagstuhl, Leibniz-Zentrum f\"ur Informatik, Dagstuhl Publishing},
    address = {Dagstuhl, Germany},
    url = {http://drops.dagstuhl.de/opus/volltexte/2015/5037}
    }
  • N. Sanchez-Pi, L. Martí, and A. C. Bicharra Garcia, “Text Classification Techniques in Oil Industry Applications,” in International Joint Conference SOCO’13-CISIS’13-ICEUTE’13, Berlin/Heidelberg, 2014, pp. 211-220. doi: 10.1007/978-3-319-01854-6_22 bibtex abstract
    @inproceedings{sanchez-2013-soco,
    Abstract = {The development of automatic methods to produce usable structured information from unstructured text sources is extremely valuable to the oil and gas industry. A structured resource would allow researches and industry professionals to write relatively simple queries to retrieve all the information regards transcriptions of any accident. Instead of the thousands of abstracts provided by querying the unstructured corpus, the queries on structured corpus would result in a few hundred well-formed results. On this paper we propose and evaluate information extraction techniques in occupational health control process, particularly, for the case of automatic detection of accidents from unstructured texts. Our proposal divides the problem in subtasks such as text analysis, recognition and classification of failed occupational health control, resolving accidents.},
    Address = {Berlin/Heidelberg},
    Author = {Sanchez-Pi, Nayat and Mart\'i, Luis and Bicharra Garcia, Ana Cristina},
    Booktitle = {International Joint Conference SOCO'13-CISIS'13-ICEUTE'13},
    Doi = {10.1007/978-3-319-01854-6_22},
    Editor = {Herrero, \'Alvaro and Baruque, Bruno and Klett, Fanny and Abraham, Ajith and Sn\'a\v{s}el, V\'aclav and de Carvalho, Andr\'e C.P.L.F. and Garc\'ia Bringas, Pablo and Zelinka, Ivan and Quintian, H\'ector and Corchado, Emilio},
    Isbn = {978-3-319-01853-9},
    Keywords = {text classification; ontology; oil and gas industry},
    Pages = {211--220},
    Publisher = {Springer International Publishing},
    Series = {Advances in Intelligent Systems and Computing},
    Title = {Text Classification Techniques in Oil Industry Applications},
    Volume = {239},
    Year = {2014}}
    The development of automatic methods to produce usable structured information from unstructured text sources is extremely valuable to the oil and gas industry. A structured resource would allow researches and industry professionals to write relatively simple queries to retrieve all the information regards transcriptions of any accident. Instead of the thousands of abstracts provided by querying the unstructured corpus, the queries on structured corpus would result in a few hundred well-formed results. On this paper we propose and evaluate information extraction techniques in occupational health control process, particularly, for the case of automatic detection of accidents from unstructured texts. Our proposal divides the problem in subtasks such as text analysis, recognition and classification of failed occupational health control, resolving accidents.
  • L. Martí, N. Sanchez-Pi, J. M. Molina, and A. C. Bicharra Garcia, “YASA: Yet Another Time Series Segmentation Algorithm for Anomaly Detection in Big Data Problems,” in Hybrid Artificial Intelligence Systems, Berlin/Heidelberg, 2014, pp. 697-708. doi: 10.1007/978-3-319-07617-1_61 bibtex abstract
    @inproceedings{marti-2014:hais,
    Abstract = {Time series patterns analysis had recently attracted the attention of the research community for real-world applications. Petroleum industry is one of the application contexts where these problems are present, for instance for anomaly detection. Offshore petroleum platforms rely on heavy turbomachines for its extraction, pumping and generation operations. Frequently, these machines are intensively monitored by hundreds of sensors each, which send measurements with a high frequency to a concentration hub. Handling these data calls for a holistic approach, as sensor data is frequently noisy, unreliable, inconsistent with a priori problem axioms, and of a massive amount. For the anomalies detection problems in turbomachinery, it is essential to segment the dataset available in order to automatically discover the operational regime of the machine in the recent past. In this paper we propose a novel time series segmentation algorithm adaptable to big data problems and that is capable of handling the high volume of data involved in problem contexts. As part of the paper we describe our proposal, analyzing its computational complexity. We also perform empirical studies comparing our algorithm with similar approaches when applied to benchmark problems and a real-life application related to oil platform turbomachinery anomaly detection.},
    Address = {Berlin/Heidelberg},
    Author = {Mart\'i, Luis and Sanchez-Pi, Nayat and Molina, Jos\'e Manuel and Bicharra Garcia, Ana Cristina},
    Booktitle = {Hybrid Artificial Intelligence Systems},
    Doi = {10.1007/978-3-319-07617-1_61},
    Editor = {Polycarpou, Marios and de Carvalho, Andr\'e C.P.L.F. and Pan, Jeng-Shyang and Wo\'{z}niak, Micha\l and Quintian, H\'ector and Corchado, Emilio},
    Isbn = {978-3-319-07616-4},
    Keywords = {Time series segmentation; anomaly detection; big data; oil industry application},
    Pages = {697--708},
    Publisher = {Springer International Publishing},
    Series = {Lecture Notes in Computer Science},
    Title = {{YASA}: {Y}et Another Time Series Segmentation Algorithm for Anomaly Detection in Big Data Problems},
    Volume = {8480},
    Year = {2014}}
    Time series patterns analysis had recently attracted the attention of the research community for real-world applications. Petroleum industry is one of the application contexts where these problems are present, for instance for anomaly detection. Offshore petroleum platforms rely on heavy turbomachines for its extraction, pumping and generation operations. Frequently, these machines are intensively monitored by hundreds of sensors each, which send measurements with a high frequency to a concentration hub. Handling these data calls for a holistic approach, as sensor data is frequently noisy, unreliable, inconsistent with a priori problem axioms, and of a massive amount. For the anomalies detection problems in turbomachinery, it is essential to segment the dataset available in order to automatically discover the operational regime of the machine in the recent past. In this paper we propose a novel time series segmentation algorithm adaptable to big data problems and that is capable of handling the high volume of data involved in problem contexts. As part of the paper we describe our proposal, analyzing its computational complexity. We also perform empirical studies comparing our algorithm with similar approaches when applied to benchmark problems and a real-life application related to oil platform turbomachinery anomaly detection.
  • L. Martí, N. Sanchez-Pi, J. M. Molina, and A. C. Bicharra Garcia, “High-Level Information Fusion for Risk and Accidents Prevention in Pervasive Oil Industry Environments,” in Highlights of Practical Applications of Heterogeneous Multi-Agent Systems — The PAAMS Collection, Berlin/Heidelberg, 2014, pp. 202-213. doi: 10.1007/978-3-319-07767-3_19 bibtex abstract
    @inproceedings{marti-2014:iscif,
    Abstract = {Information fusion studies theories and methods to effectively combine data from multiple sensors and related information to achieve more specific inferences that could be achieved by using a single, independent sensor. Information fused from sensors and data mining analysis has recently attracted the attention of the research community for real-world applications. In this sense, the deployment of an Intelligent Offshore Oil Industry Environment will help to figure out a risky scenario based on the events occurred in the past related to anomalies and the profile of the current employee (role, location, etc.). In this paper we propose an information fusion model for an intelligent oil environment in which employees are alerted about possible risk situations while their are moving around their working place. The layered architecture, implements a reasoning engine capable of intelligently filtering the context profile of the employee (role, location) for the feature selection of an inter-transaction mining process. Depending on the employee contextual information he will receive intelligent alerts based on the prediction model that use his role and his current location. This model provides the big picture about risk analysis for that employee at that place in that moment.},
    Address = {Berlin/Heidelberg},
    Author = {Mart\'{i}, Luis and Nayat Sanchez-Pi and Molina, Jos\'{e} Manuel and Bicharra Garcia, Ana Cristina},
    Booktitle = {Highlights of Practical Applications of Heterogeneous Multi-Agent Systems --- The PAAMS Collection},
    Doi = {10.1007/978-3-319-07767-3_19},
    Editor = {Corchado, Juan M. and Bajo, Javier and Kozlak, Jaroslaw and Pawlewski, Pawel and Molina, Jos\'e Manuel and Gaudou, Benoit and Julian, Vicente and Unland, Rainer and Lopes, Fernando and Hallenborg, Kasper and Garc\'ia Teodoro, Pedro},
    Isbn = {978-3-319-07766-6},
    Keywords = {Information fusion; context; data mining; ontologies; oil industry},
    Pages = {202--213},
    Publisher = {Springer International Publishing},
    Series = {Communications in Computer and Information Science},
    Title = {High-Level Information Fusion for Risk and Accidents Prevention in Pervasive Oil Industry Environments},
    Volume = {430},
    Year = {2014}}
    Information fusion studies theories and methods to effectively combine data from multiple sensors and related information to achieve more specific inferences that could be achieved by using a single, independent sensor. Information fused from sensors and data mining analysis has recently attracted the attention of the research community for real-world applications. In this sense, the deployment of an Intelligent Offshore Oil Industry Environment will help to figure out a risky scenario based on the events occurred in the past related to anomalies and the profile of the current employee (role, location, etc.). In this paper we propose an information fusion model for an intelligent oil environment in which employees are alerted about possible risk situations while their are moving around their working place. The layered architecture, implements a reasoning engine capable of intelligently filtering the context profile of the employee (role, location) for the feature selection of an inter-transaction mining process. Depending on the employee contextual information he will receive intelligent alerts based on the prediction model that use his role and his current location. This model provides the big picture about risk analysis for that employee at that place in that moment.
  • N. Sanchez-Pi, L. Martí, J. M. Molina, and A. C. Bicharra Garcia, “An information fusion framework for context-based accidents prevention,” in 17th International Conference on Information Fusion (FUSION), 2014, pp. 1-8. bibtex abstract url
    @inproceedings{sanchez-2014:fusion,
    Abstract = {The oil and gas industry is increasingly concerned with achieving and demonstrating good performance with regard occupational health and safety (OHS) issues, through the control of its OHS risks, which is consistent with its core policy and objectives. There are standards to identify and record workplace accidents and incidents to provide guiding means on prevention efforts, indicating specific failures or reference, means of correction of conditions or circumstances that culminated in an accident. Therefore, events recognition is central to OHS, since the system can selectively start proper prediction services according to the user current situation and past knowledge taken from huge databases. In this sense, a fusion framework that combines data from multiples sources to achieve more specific inferences is needed. In this paper we propose a machine learning algorithm to learn from past anomalous events related to accident events in time and space. It also uses additional knowledge, like the contextual knowledge: user profile, event location and time, etc. Our proposed model provides the big picture about risk analysis for that employee at that place in that moment in a real world environment. Our main contribution lies in building a causality model for accident investigation by means of well-defined spatiotemporal constraints in the offshore oil industry domain.},
    Author = {Sanchez-Pi, Nayat and Mart\'{i}, Luis and Molina, Jos\'{e} Manuel and Bicharra Garcia, Ana Cristina},
    Booktitle = {17th International Conference on Information Fusion (FUSION)},
    Month = {7},
    Pages = {1-8},
    Title = {An information fusion framework for context-based accidents prevention},
    Url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6916105&tag=1},
    Year = {2014}}
    The oil and gas industry is increasingly concerned with achieving and demonstrating good performance with regard occupational health and safety (OHS) issues, through the control of its OHS risks, which is consistent with its core policy and objectives. There are standards to identify and record workplace accidents and incidents to provide guiding means on prevention efforts, indicating specific failures or reference, means of correction of conditions or circumstances that culminated in an accident. Therefore, events recognition is central to OHS, since the system can selectively start proper prediction services according to the user current situation and past knowledge taken from huge databases. In this sense, a fusion framework that combines data from multiples sources to achieve more specific inferences is needed. In this paper we propose a machine learning algorithm to learn from past anomalous events related to accident events in time and space. It also uses additional knowledge, like the contextual knowledge: user profile, event location and time, etc. Our proposed model provides the big picture about risk analysis for that employee at that place in that moment in a real world environment. Our main contribution lies in building a causality model for accident investigation by means of well-defined spatiotemporal constraints in the offshore oil industry domain.
  • L. Martí, N. Sanchez-Pi, and M. M. ~B. Rebuzzi Vellasco, “Understanding the Treatment of Outliers in Multi-Objective Estimation of Distribution Algorithms,” in Advances in Artificial Intelligence — IBERAMIA 2014, Heidelberg/Berlin, 2014, pp. 359-370. doi: 10.1007/978-3-319-12027-0_29 bibtex abstract
    @inproceedings{marti-2014:outliers,
    Abstract = {It has been already documented the fact that estimation of distribution algorithms suffer from loss of population diversity and improper treatment of isolated solutions. This situation is particularly severe in the case of multi-objective optimization, as the loss of solution diversity limits the capacity of an algorithm to explore the Pareto-optimal front at full extent. A set of approaches has been proposed to deal with this problem but ---to the best of our knowledge--- there has not been a comprehensive comparative study on the outcome of those solutions and at what degree they actually solve the issue. This paper puts forward such study by comparing how current approaches handle diversity loss when confronted to different multi-objective problems.},
    Address = {Heidelberg/Berlin},
    Author = {Mart\'{i}, Luis and Sanchez-Pi, Nayat and Rebuzzi Vellasco, Marley M.~B.},
    Booktitle = {Advances in Artificial Intelligence --- IBERAMIA 2014},
    Doi = {10.1007/978-3-319-12027-0_29},
    Editor = {Bazzan, Ana L.C. and Pichara, Karim},
    Isbn = {978-3-319-12026-3},
    Keywords = {Multi-objective optimization; Estimation of distribution algorithms; Model building; Outlier detection},
    Pages = {359--370},
    location = {Santiago de Chile, Chile},
    Publisher = {Springer International Publishing},
    Series = {Lecture Notes in Computer Science},
    Title = {Understanding the Treatment of Outliers in Multi-Objective Estimation of Distribution Algorithms},
    Year = {2014}}
    It has been already documented the fact that estimation of distribution algorithms suffer from loss of population diversity and improper treatment of isolated solutions. This situation is particularly severe in the case of multi-objective optimization, as the loss of solution diversity limits the capacity of an algorithm to explore the Pareto-optimal front at full extent. A set of approaches has been proposed to deal with this problem but —to the best of our knowledge— there has not been a comprehensive comparative study on the outcome of those solutions and at what degree they actually solve the issue. This paper puts forward such study by comparing how current approaches handle diversity loss when confronted to different multi-objective problems.
  • L. Martí, N. Sanchez-Pi, J. M. Molina, and A. C. Bicharra Garcia, “Combining Support Vector Machines and Segmentation Algorithms for Eficient Anomaly Detection: A Petroleum Industry Application,” in 9th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2014), Berlin/Heidelberg, 2014, pp. 269-278. doi: 10.1007/978-3-319-07995-0_27 bibtex abstract
    @inproceedings{marti-2014:soco,
    Abstract = {Anomaly detection is the problem of finding patterns in data that do not conform to expected behavior. Similarly, when patterns are numerically distant from the rest of sample, anomalies are indicated as outliers. Anomaly detection had recently attracted the attention of the research community for real-world applications. The petroleum industry is one of the application contexts where these problems are present. The correct detection of such types of unusual information empowers the decision maker with the capacity to act on the system in order to correctly avoid, correct, or react to the situations associated with them. In that sense, heavy extraction machines for pumping and generation operations like turbomachines are intensively monitored by hundreds of sensors each that send measurements with a high frequency for damage prevention. For dealing with this and with the lack of labeled data, in this paper we propose a combination of a fast and high quality segmentation algorithm with a one-class support vector machine approach for efficient anomaly detection in turbomachines. As result we perform empirical studies comparing our approach to other methods applied to benchmark problems and a real-life application related to oil platform turbomachinery anomaly detection.},
    Address = {Berlin/Heidelberg},
    Author = {Mart\'{i}, Luis and Nayat Sanchez-Pi and Molina, Jos\'{e} Manuel and Bicharra Garcia, Ana Cristina},
    Booktitle = {9th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2014)},
    Doi = {10.1007/978-3-319-07995-0_27},
    Editor = {de la Puerta, Jos\'{e} Gaviria and Ferreira, Iv\'{a}n Garc\'{i}a and Garc\'{i}a Bringas, Pablo and Klett, Fanny and Abraham, Ajith and de Carvalho, Andr\'{e} C.P.L.F. and Herrero, \'{A}lvaro and Baruque, Bruno and Quinti\'an, H\'{e}ctor and Corchado, Emilio},
    Location = {Salamanca, Spain},
    Pages = {269--278},
    Publisher = {Springer},
    Series = {Advances in Intelligent Systems and Computing},
    Title = {Combining Support Vector Machines and Segmentation Algorithms for Eficient Anomaly Detection: {A} Petroleum Industry Application},
    Volume = {299},
    Year = {2014}}
    Anomaly detection is the problem of finding patterns in data that do not conform to expected behavior. Similarly, when patterns are numerically distant from the rest of sample, anomalies are indicated as outliers. Anomaly detection had recently attracted the attention of the research community for real-world applications. The petroleum industry is one of the application contexts where these problems are present. The correct detection of such types of unusual information empowers the decision maker with the capacity to act on the system in order to correctly avoid, correct, or react to the situations associated with them. In that sense, heavy extraction machines for pumping and generation operations like turbomachines are intensively monitored by hundreds of sensors each that send measurements with a high frequency for damage prevention. For dealing with this and with the lack of labeled data, in this paper we propose a combination of a fast and high quality segmentation algorithm with a one-class support vector machine approach for efficient anomaly detection in turbomachines. As result we perform empirical studies comparing our approach to other methods applied to benchmark problems and a real-life application related to oil platform turbomachinery anomaly detection.
  • N. Sanchez-Pi, L. Martí, and A. C. Bicharra Garcia, “Information Extraction Techniques for Health, Safety and Environment Applications in Oil Industry,” in IADIS Intelligent Systems and Agents 2013 (ISA 2013), 2013. bibtex
    @inproceedings{sanchez-2013a,
    Author = {Sanchez-Pi, Nayat and Mart\'i, Luis and Bicharra Garcia, Ana Cristina},
    Booktitle = {IADIS Intelligent Systems and Agents 2013 (ISA 2013)},
    Title = {Information Extraction Techniques for Health, Safety and Environment Applications in Oil Industry},
    Year = {2013}}
  • N. Sanchez-Pi, L. Martí, and A. C. Bicharra Garcia, “Information Extraction Techniques for Health, Safety and Environment Applications in Oil Industry,” in International Conference on Artificial Intelligence (ICAI 2013), 2013. bibtex
    @inproceedings{sanchez-2013:icai,
    Author = {Nayat Sanchez-Pi and Luis Mart\'i and Bicharra Garcia, Ana Cristina},
    Booktitle = {International Conference on Artificial Intelligence (ICAI 2013)},
    Title = {Information Extraction Techniques for Health, Safety and Environment Applications in Oil Industry},
    Year = {2013}}
  • T. Wagner, H. Trautmann, and L. Martí, “A Taxonomy of Online Stopping Criteria for Multi-Objective Evolutionary Algorithms,” in 6th International Conference on Evolutionary Multi-Criterion Optimization (EMO 2011), Berlin/Heidelberg, 2011, pp. 16-30. doi: 10.1007/978-3-642-19893-9_2 bibtex abstract
    @inproceedings{wagner-2011:tax,
    Abstract = {The use of multi-objective evolutionary algorithms for solving black-box problems with multiple conflicting objectives has become an important research area. However, when no gradient information is available, the examination of formal convergence or optimality criteria is often impossible. Thus, sophisticated heuristic online stopping criteria (OSC) have recently become subject of intensive research. In order to establish formal guidelines for a systematic research, we present a taxonomy of OSC in this paper. We integrate the known approaches within the taxonomy and discuss them by extracting their building blocks. The formal structure of the taxonomy is used as a basis for the implementation of a comprehensive MATLAB toolbox. Both contributions, the formal taxonomy and the MATLAB implementation, provide a framework for the analysis and evaluation of existing and new OSC approaches.},
    Address = {Berlin/Heidelberg},
    Author = {Wagner, Tobias and Trautmann, Heike and Mart\'{i}, Luis},
    Booktitle = {6th International Conference on Evolutionary Multi-Criterion Optimization (EMO 2011)},
    Editor = {Takahashi, Ricardo H. C. and Kalyanmoy Deb and Wanner, Elizabeth F. and Salvatore Greco},
    Isbn = {978-3-642-19892-2},
    Location = {Ouro Preto (MG) Brazil},
    doi = {10.1007/978-3-642-19893-9_2},
    Pages = {16--30},
    Publisher = {Springer},
    Title = {A Taxonomy of Online Stopping Criteria for Multi-Objective Evolutionary Algorithms},
    Volume = {6576},
    Year = {2011}}
    The use of multi-objective evolutionary algorithms for solving black-box problems with multiple conflicting objectives has become an important research area. However, when no gradient information is available, the examination of formal convergence or optimality criteria is often impossible. Thus, sophisticated heuristic online stopping criteria (OSC) have recently become subject of intensive research. In order to establish formal guidelines for a systematic research, we present a taxonomy of OSC in this paper. We integrate the known approaches within the taxonomy and discuss them by extracting their building blocks. The formal structure of the taxonomy is used as a basis for the implementation of a comprehensive MATLAB toolbox. Both contributions, the formal taxonomy and the MATLAB implementation, provide a framework for the analysis and evaluation of existing and new OSC approaches.
  • L. Martí, J. García, A. Berlanga, and J. M. Molina, “Indicator-based MONEDA: A Comparative Study of Scalability with Respect to Decision Space Dimensions,” in 2011 IEEE Conference on Evolutionary Computation (CEC), Piscataway, New Jersey, 2011, pp. 957-964. doi: 10.1109/CEC.2011.5949721 bibtex abstract
    @inproceedings{marti-2011:indicator-moneda,
    Abstract = {The multi-objective neural EDA (MONEDA) was
    proposed with the aim of overcoming some difficulties of current MOEDAs. MONEDA has been shown to yield relevant
    results when confronted with complex problems. Furthermore, its
    performance has been shown to adequately adapt to problems
    with many objectives. Nevertheless, one key issue remains to
    be studied: MONEDA scalability with regard to the number of
    decision variables.
    In this paper has a two-fold purpose. On one hand we propose
    a modification of MONEDA that incorporates an indicator-based
    selection mechanism based on the HypE algorithm, while, on
    the other, we assess the indicator-based MONEDA when solving
    some complex two-objective problems, in particular problems
    UF1 to UF7 of the CEC 2009 MOP competition, configured with
    a progressively-increasing number of decision variables.},
    Address = {Piscataway, New Jersey},
    Author = {Mart\'{i}, Luis and Garc\'{i}a, Jes\'{u}s and Berlanga, Antonio and Molina, Jos\'{e} Manuel},
    Booktitle = {2011 IEEE Conference on Evolutionary Computation (CEC)},
    Doi = {10.1109/CEC.2011.5949721},
    Location = {New Orleans, Louisiana},
    Pages = {957--964},
    Publisher = {IEEE Press},
    Title = {Indicator-based {MONEDA}: {A} Comparative Study of Scalability with Respect to Decision Space Dimensions},
    Year = {2011}}
    The multi-objective neural EDA (MONEDA) was proposed with the aim of overcoming some difficulties of current MOEDAs. MONEDA has been shown to yield relevant results when confronted with complex problems. Furthermore, its performance has been shown to adequately adapt to problems with many objectives. Nevertheless, one key issue remains to be studied: MONEDA scalability with regard to the number of decision variables. In this paper has a two-fold purpose. On one hand we propose a modification of MONEDA that incorporates an indicator-based selection mechanism based on the HypE algorithm, while, on the other, we assess the indicator-based MONEDA when solving some complex two-objective problems, in particular problems UF1 to UF7 of the CEC 2009 MOP competition, configured with a progressively-increasing number of decision variables.
  • L. Martí, J. García, A. Berlanga, and J. M. Molina, “Moving away from error-based learning in multi-objective estimation of distribution algorithms,” in GECCO’10: Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation, New York, NY, USA, 2010, pp. 545-546. doi: 10.1145/1830483.1830585 bibtex url
    @inproceedings{marti-2010:marteda,
    Address = {New York, NY, USA},
    Author = {Mart\'{i}, Luis and Garc\'{i}a, Jes\'{u}s and Berlanga, Antonio and Molina, Jos\'{e} Manuel},
    Booktitle = {GECCO'10: Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation},
    Date-Modified = {2010-12-22 23:45:42 +0100},
    Doi = {10.1145/1830483.1830585},
    Editor = {Branke, J. and Alba, E. and Arnold, D. and Bongard, J. and Brabazon, A. and Butz, M.~V. and Clune, J. and Cohen, M. and Deb, K. and Engelbrecht, A. and Krasnogor, N. and Miller, J.F. and O'Neill, M. and Sastry, K. and Thierens, D. and Vanneschi, L. and van Hemert, J. and Witt, C.},
    Isbn = {978-1-4503-0072-8},
    Location = {Portland, Oregon, USA},
    Pages = {545--546},
    Publisher = {ACM Press},
    Title = {Moving away from error-based learning in multi-objective estimation of distribution algorithms},
    Url = {http://doi.acm.org/10.1145/1830483.1830585},
    Year = {2010}}
  • L. Martí, J. García, A. Berlanga, and J. M. Molina, “A Progress Indicator for Detecting Success and Failure in Evolutionary Multi–Objective Optimization,” in 2010 IEEE Conference on Evolutionary Computation (CEC), part of 2010 IEEE World Congress on Computational Intelligence (WCCI 2010), Piscataway, New Jersey, 2010. doi: 10.1109/CEC.2010.5586352 bibtex abstract
    @inproceedings{marti-2010:stability,
    Abstract = {In this work we present a novel progress indicator, called fitness homogeneity indicator (FHI). This indicator improves the other previously discussed indicators as it takes into account all possible processes taking place in the population while not requiring an intensive computation as it relies on the fitness values calculated for the individuals. It is also capable of equally detecting success and failure scenarios, hopefully making an early detection of the second case.},
    Address = {Piscataway, New Jersey},
    Author = {Mart\'{i}, Luis and Garc\'{i}a, Jes\'{u}s and Berlanga, Antonio and Molina, Jos\'{e} Manuel},
    Booktitle = {2010 IEEE Conference on Evolutionary Computation (CEC), part of 2010 IEEE World Congress on Computational Intelligence (WCCI 2010)},
    Date-Modified = {2010-12-22 23:48:04 +0100},
    Doi = {10.1109/CEC.2010.5586352},
    File = {:papers:marti-cec-2010.pdf},
    Location = {Barcelona, Spain},
    Publisher = {IEEE Press},
    Title = {A Progress Indicator for Detecting Success and Failure in Evolutionary Multi--Objective Optimization},
    Year = {2010}}
    In this work we present a novel progress indicator, called fitness homogeneity indicator (FHI). This indicator improves the other previously discussed indicators as it takes into account all possible processes taking place in the population while not requiring an intensive computation as it relies on the fitness values calculated for the individuals. It is also capable of equally detecting success and failure scenarios, hopefully making an early detection of the second case.
  • J. Guerrero, L. Martí, J. García, A. Berlanga, and J. M. Molina, “Introducing a Robust and Efficient Stopping Criterion for MOEAs,” in 2010 IEEE Conference on Evolutionary Computation (CEC), part of 2010 IEEE World Congress on Computational Intelligence (WCCI 2010), Piscataway, New Jersey, 2010. doi: 10.1109/CEC.2010.5586265 bibtex abstract
    @inproceedings{guerrero-2010:lssc,
    Abstract = {Soft computing methods, and Multi-Objective Evolutionary Algorithms (MOEAs) in particular, lack a general convergence criterion which prevents these algorithms from detecting the generation where further evolution will provide little improvements (or none at all) over the current solution, making them waste computational resources. This paper presents the Least Squares Stopping Criterion (LSSC), an easily configurable and implementable, robust and efficient stopping criterion, based on simple statistical parameters and residue analysis, which tries to introduce as few setup parameters as possible, being them always related to the MOEAs research field rather than the techniques applied by the criterion.},
    Address = {Piscataway, New Jersey},
    Author = {Guerrero, Jos\'{e}~L. and Mart\'{i}, Luis and Garc\'{i}a, Jes\'{u}s and Berlanga, Antonio and Molina, Jos\'{e} Manuel},
    Booktitle = {2010 IEEE Conference on Evolutionary Computation (CEC), part of 2010 IEEE World Congress on Computational Intelligence (WCCI 2010)},
    Date-Modified = {2010-12-22 23:49:03 +0100},
    Doi = {10.1109/CEC.2010.5586265},
    File = {:papers:guerrero-cec-2010.pdf},
    Location = {Barcelona, Spain},
    Publisher = {IEEE Press},
    Title = {Introducing a Robust and Efficient Stopping Criterion for {MOEAs}},
    Year = {2010}}
    Soft computing methods, and Multi-Objective Evolutionary Algorithms (MOEAs) in particular, lack a general convergence criterion which prevents these algorithms from detecting the generation where further evolution will provide little improvements (or none at all) over the current solution, making them waste computational resources. This paper presents the Least Squares Stopping Criterion (LSSC), an easily configurable and implementable, robust and efficient stopping criterion, based on simple statistical parameters and residue analysis, which tries to introduce as few setup parameters as possible, being them always related to the MOEAs research field rather than the techniques applied by the criterion.
  • L. Martí, J. García, A. Berlanga, and J. M. Molina, “Advancing Model-Building for Many-Objective Optimization Estimation of Distribution Algorithms,” in Applications of Evolutionary Computation, 2010, pp. 512-521. doi: 10.1007/978-3-642-12239-2_53 bibtex url
    @inproceedings{marti-2010:advancing-model-building,
    Author = {Mart\'{i}, Luis and Garc\'{i}a, Jes\'{u}s and Berlanga, Antonio and Molina, Jos\'{e} Manuel},
    Booktitle = {Applications of Evolutionary Computation},
    Date-Modified = {2010-12-22 23:46:47 +0100},
    Doi = {10.1007/978-3-642-12239-2_53},
    Editor = {Chio, Cecilia Di and Cagnoni, Stefano and Cotta, Carlos and Ebner, Marc and Ek\'{a}rt, Anik\'{o} and Esparcia-Alcazar, Anna I. and Goh, Chi-Keong and Merelo, Juan J. and Neri, Ferrante and Preu{\ss}, Mike and Togelius, Julian and Yannakakis, Georgios N.},
    Isbn = {978-3-642-12238-5},
    Location = {Heidelberg/Berlin},
    Pages = {512--521},
    Publisher = {Springer},
    Series = {Lecture Notes in Computer Science},
    Title = {Advancing Model-Building for Many-Objective Optimization Estimation of Distribution Algorithms},
    Url = {http://www.springerlink.com/content/cg4nu92878524r23/},
    Volume = {6024},
    Year = {2010}}
  • L. Martí, J. García, A. Berlanga, and J. M. Molina, “An Approach to Stopping Criteria for Multi–Objective Optimization Evolutionary Algorithms: The MGBM Criterion,” in 2009 IEEE Conference on Evolutionary Computation (CEC 2009), Piscataway, New Jersey, 2009, pp. 1263-1270. doi: 10.1109/CEC.2009.4983090 bibtex abstract
    @inproceedings{marti-2009:stopping-cec,
    Abstract = {In this work we put forward a comprehensive study on the design of global stopping criteria for multi-objective optimization. We describe a novel stopping criterion, denominated MGBM criterion that combines the mutual domination rate (MDR) improvement indicator with a simplified Kalman filter that is used for evidence gathering process. The MDR indicator, which is introduced along, is a special purpose solution meant for the stopping task. It is capable of gauging the progress of the optimization with a low computational cost and therefore suitable for solving complex or many-objective problems. The viability of the proposal is established by comparing it with some other possible alternatives. It should be noted that, although the criteria discussed here are meant for MOPs and MOEAs, they could be easily adapted to other softcomputing or numerical methods by substituting the local improvement metric with a suitable one.},
    Address = {Piscataway, New Jersey},
    Author = {Mart\'{i}, Luis and Garc\'{i}a, Jes\'{u}s and Berlanga, Antonio and Molina, Jos\'{e} Manuel},
    Booktitle = {2009 IEEE Conference on Evolutionary Computation (CEC 2009)},
    Date-Modified = {2010-12-22 23:53:43 +0100},
    Doi = {10.1109/CEC.2009.4983090},
    Isbn = {978-1-4244-2959-2},
    Location = {Trondheim, Norway},
    Pages = {1263--1270},
    Publisher = {IEEE Press},
    Title = {An Approach to Stopping Criteria for Multi--Objective Optimization Evolutionary Algorithms: {T}he {MGBM} Criterion},
    Year = {2009}}
    In this work we put forward a comprehensive study on the design of global stopping criteria for multi-objective optimization. We describe a novel stopping criterion, denominated MGBM criterion that combines the mutual domination rate (MDR) improvement indicator with a simplified Kalman filter that is used for evidence gathering process. The MDR indicator, which is introduced along, is a special purpose solution meant for the stopping task. It is capable of gauging the progress of the optimization with a low computational cost and therefore suitable for solving complex or many-objective problems. The viability of the proposal is established by comparing it with some other possible alternatives. It should be noted that, although the criteria discussed here are meant for MOPs and MOEAs, they could be easily adapted to other softcomputing or numerical methods by substituting the local improvement metric with a suitable one.
  • L. Martí, J. García, A. Berlanga, and J. M. Molina, “On the Computational Properties of the Multi–objective Neural Estimation of Distribution Algorithms,” in Nature Inspired Cooperative Strategies for Optimization (NICSO 2008), Berlin/Heidelberg, 2009, pp. 239-251. doi: 10.1007/978-3-642-03211-0_20 bibtex abstract
    @inproceedings{marti-2008:moneda-comp-cost,
    Abstract = {This paper explores the behavior of the multi-objective neural EDA (MONEDA) in terms of its computational requirements it demands and assesses how it scales when dealing with multi-objective optimization problems with relatively large amounts of objectives. In order to properly comprehend these matters other MOEDAs and MOEAs are included in the analysis. The experiments performed tested the ability of each approach to scalably solve many-objective optimization problems. The fundamental result obtained is that MONEDA is not only yields similar or better solutions when compared with other approaches but also does it with at a lower computational cost.},
    Address = {Berlin/Heidelberg},
    Author = {Mart\'{i}, Luis and Garc\'{i}a, Jes\'{u}s and Berlanga, Antonio and Molina, Jos\'{e} Manuel},
    Booktitle = {Nature Inspired Cooperative Strategies for Optimization (NICSO 2008)},
    Date-Modified = {2010-12-22 23:56:08 +0100},
    Doi = {10.1007/978-3-642-03211-0_20},
    Editor = {Krasnogor, N. and Meli\'{a}n-Batista, B. and Moreno-P\'{e}rez, J.~A. and Moreno-Vega, J.~M. and Pelta, D.},
    Isbn = {978--3--642--03210--3},
    Pages = {239--251},
    Publisher = {Springer},
    Series = {Studies in Computational Intelligence},
    Title = {On the Computational Properties of the Multi--objective Neural Estimation of Distribution Algorithms},
    Volume = {236},
    Year = {2009}}
    This paper explores the behavior of the multi-objective neural EDA (MONEDA) in terms of its computational requirements it demands and assesses how it scales when dealing with multi-objective optimization problems with relatively large amounts of objectives. In order to properly comprehend these matters other MOEDAs and MOEAs are included in the analysis. The experiments performed tested the ability of each approach to scalably solve many-objective optimization problems. The fundamental result obtained is that MONEDA is not only yields similar or better solutions when compared with other approaches but also does it with at a lower computational cost.
  • L. Martí, J. García, A. Berlanga, and J. M. Molina, “On the Model-Building Issue of Multi-Objective Estimation of Distribution Algorithms,” in 4th International Conference on Hybrid Artificial Intelligence (HAIS’09), Berlin/Heidelberg, 2009, pp. 293-300. doi: 10.1007/978-3-642-02319-4_35 bibtex abstract
    @inproceedings{marti-2009:eda-directions,
    Abstract = {It has been claimed that perhaps a paradigm shift is necessary in order to be able to deal with this scalability issue of multi-objective optimization evolutionary algorithms. Estimation of distribution algorithms are viable candidates for such task because of their adaptation and learning abilities and simplified algorithmics. Nevertheless, the extension of EDAs to the multi-objective domain have not provided a significant improvement over MOEAs.
    In this paper we analyze the possible causes of this underachievement and propose a set of measures that should be taken in order to overcome the current situation.},
    Address = {Berlin/Heidelberg},
    Author = {Mart\'{i}, Luis and Garc\'{i}a, Jes\'{u}s and Berlanga, Antonio and Molina, Jos\'{e} Manuel},
    Booktitle = {4th International Conference on Hybrid Artificial Intelligence (HAIS'09)},
    Date-Modified = {2010-12-22 23:52:40 +0100},
    Doi = {10.1007/978-3-642-02319-4_35},
    Editor = {Corchado, Emilio and Wu, Xindong and Oja, Erkki and Herrero, \'{A}lvaro and Baruque, Bruno},
    Location = {Salamanca, Spain},
    Pages = {293--300},
    Publisher = {Springer},
    Series = {Lecture Notes in Artificial Intelligence},
    Title = {On the Model-Building Issue of Multi-Objective Estimation of Distribution Algorithms},
    Volume = {5572},
    Year = {2009}}
    It has been claimed that perhaps a paradigm shift is necessary in order to be able to deal with this scalability issue of multi-objective optimization evolutionary algorithms. Estimation of distribution algorithms are viable candidates for such task because of their adaptation and learning abilities and simplified algorithmics. Nevertheless, the extension of EDAs to the multi-objective domain have not provided a significant improvement over MOEAs. In this paper we analyze the possible causes of this underachievement and propose a set of measures that should be taken in order to overcome the current situation.
  • J. L. Guerrero, J. García, L. Martí, J. M. Molina, and A. Berlanga, “A Stopping Criterion Based on Kalman Estimation Techniques with Several Progress Indicators,” in GECCO’09: 11th Annual Conference on Genetic and Evolutionary Computation, New York, NY, USA, 2009, pp. 587-594. doi: 10.1145/1569901.1569983 bibtex abstract url
    @inproceedings{guerrero-2009:stopping,
    Abstract = {The need for a stopping criterion in MOEA's is a repeatedly mentioned matter in the domain of MOOP's, even though it is usually left aside as secondary, while stopping criteria are still usually based on an a-priori chosen number of maximum iterations. In this paper we want to present a stopping criterion for MOEA's based on three different indicators already present in the community. These indicators, some of which were originally designed for solution quality measuring (as a function of the distance to the optimal Pareto front), will be processed so they can be applied as part of a global criterion, based on estimation theory to achieve a cumulative evidence measure to be used in the stopping decision (by means of a Kalman filter). The implications of this cumulative evidence are analyzed, to get a problem and algorithm independent stopping criterion (for each individual indicator). Finally, the stopping criterion is presented from a data fusion perspective, using the different individual indicators' stopping criteria together, in order to get a final global stopping criterion.},
    Address = {New York, NY, USA},
    Author = {Guerrero, Jos\'{e} Luis and Garc\'{i}a, Jes\'{u}s and Mart\'{i}, Luis and Molina, Jos\'{e} Manuel and Berlanga, Antonio},
    Booktitle = {GECCO'09: 11th Annual Conference on Genetic and Evolutionary Computation},
    Date-Modified = {2010-12-22 23:41:55 +0100},
    Doi = {10.1145/1569901.1569983},
    Editor = {Raidl, G. and Alba, E. and Bacardit, J. and Bates Congdon, C. and Beyer, H.-G. and Birattari, M. and Blum, C. and Bosman, P.~A.~N. and Corne, D. and Cotta, C. and Di Penta, M. and Doerr, B. and Drechsler, R. and Ebner, M. and Grahl, J. and Jansen, T. and Knowles, J. and Lenaerts, T. and Middendorf, M. and Miller, J. ~F. and O'Neill, M. and Poli, R. and Squillero, G. and Stanley, K. and St\"utzle, T. and van Hemert, J.},
    File = {:papers:guerrero-gecco-2009.pdf},
    Isbn = {978-1-60558-325-9},
    Location = {Montreal, Qu\'{e}bec, Canada},
    Pages = {587--594},
    Publisher = {ACM Press},
    Title = {A Stopping Criterion Based on {K}alman Estimation Techniques with Several Progress Indicators},
    Url = {http://portal.acm.org/citation.cfm?id=1569983},
    Year = {2009}}
    The need for a stopping criterion in MOEA’s is a repeatedly mentioned matter in the domain of MOOP’s, even though it is usually left aside as secondary, while stopping criteria are still usually based on an a-priori chosen number of maximum iterations. In this paper we want to present a stopping criterion for MOEA’s based on three different indicators already present in the community. These indicators, some of which were originally designed for solution quality measuring (as a function of the distance to the optimal Pareto front), will be processed so they can be applied as part of a global criterion, based on estimation theory to achieve a cumulative evidence measure to be used in the stopping decision (by means of a Kalman filter). The implications of this cumulative evidence are analyzed, to get a problem and algorithm independent stopping criterion (for each individual indicator). Finally, the stopping criterion is presented from a data fusion perspective, using the different individual indicators’ stopping criteria together, in order to get a final global stopping criterion.
  • L. Martí, J. García, A. Berlanga, and J. M. Molina, “On the influence of outliers on the loss of population diversity of multi–objective estimation of distribution algorithms,” in Taller Latino Iberoamericano de Investigación de Operaciones, Acapulco (Gro), Mexico, 2009. bibtex
    @inproceedings{marti-2009:acapulco,
    Address = {Acapulco (Gro), Mexico},
    Author = {Mart\'{i}, Luis and Garc\'{i}a, Jes\'{u}s and Berlanga, Antonio and Molina, Jos\'{e} Manuel},
    Booktitle = {Taller Latino Iberoamericano de Investigaci\'on de Operaciones},
    Editor = {Sautto Vallejo, J. Maclovio and Pe\~na Galeana, Ricardo and Valdivia Noyola, Petra and Pe\~na Galeana, Norma I.},
    Isbn = {978-607-7760-20-7},
    Location = {Acapulco (Gro), Mexico},
    Publisher = {Universidad Aut\'onoma de Guerrero},
    Title = {On the influence of outliers on the loss of population diversity of multi--objective estimation of distribution algorithms},
    Year = {2009}}
  • L. Martí, J. García, A. Berlanga, and J. M. Molina, “Solving Complex High-Dimensional Problems with the Multi-Objective Neural Estimation of Distribution Algorithm,” in GECCO’09: 11th Annual Conference on Genetic and Evolutionary Computation, New York, NY, USA, 2009, pp. 619-626. doi: 10.1145/1569901.1569987 bibtex abstract url
    @inproceedings{marti-2009:moneda-wfg,
    Abstract = {The multi-objective optimization neural estimation of distribution algorithm (MONEDA) was devised with the purpose of dealing with the model-building issues of MOEDAs and, therefore address their scalability.
    In this paper we put forward a comprehensive set of experiments that intends to compare MONEDA with similar approaches when solving complex community accepted MOPs. In particular, we deal with the Walking Fish Group scalable test problem set (WFG). These tests aim to establish the optimizing capacity of MONEDA and the consistency as an optimization method.
    The fundamental conclusion of these assessment is that we provide strong evidences of the viability of MONEDA for handling hard and complex high-dimensional problems and its superior performance when compared to similar approaches. In spite of the fact that obviously further studies are necessary, these extensive experiments have provided solid ground for the use of MONEDA in more ambitious real-world applications.},
    Address = {New York, NY, USA},
    Author = {Mart\'{i}, Luis and Garc\'{i}a, Jes\'{u}s and Berlanga, Antonio and Molina, Jos\'{e} Manuel},
    Booktitle = {GECCO'09: 11th Annual Conference on Genetic and Evolutionary Computation},
    Date-Modified = {2010-12-22 23:50:00 +0100},
    Doi = {10.1145/1569901.1569987},
    Editor = {Raidl, G. and Alba, E. and Bacardit, J. and Bates Congdon, C. and Beyer, H.-G. and Birattari, M. and Blum, C. and Bosman, P.~A.~N. and Corne, D. and Cotta, C. and Di Penta, M. and Doerr, B. and Drechsler, R. and Ebner, M. and Grahl, J. and Jansen, T. and Knowles, J. and Lenaerts, T. and Middendorf, M. and Miller, J. ~F. and O'Neill, M. and Poli, R. and Squillero, G. and Stanley, K. and St\"utzle, T. and van Hemert, J.},
    File = {:papers:marti-gecco-2009.pdf},
    Isbn = {978-1-60558-325-9},
    Location = {Montreal, Qu\'{e}bec, Canada},
    Pages = {619--626},
    Publisher = {ACM Press},
    Title = {Solving Complex High-Dimensional Problems with the Multi-Objective Neural Estimation of Distribution Algorithm},
    Url = {http://portal.acm.org/citation.cfm?id=1569901.1569987},
    Year = {2009},
    Bdsk-Url-1 = {http://portal.acm.org/citation.cfm?id=1569901.1569987}}
    The multi-objective optimization neural estimation of distribution algorithm (MONEDA) was devised with the purpose of dealing with the model-building issues of MOEDAs and, therefore address their scalability. In this paper we put forward a comprehensive set of experiments that intends to compare MONEDA with similar approaches when solving complex community accepted MOPs. In particular, we deal with the Walking Fish Group scalable test problem set (WFG). These tests aim to establish the optimizing capacity of MONEDA and the consistency as an optimization method. The fundamental conclusion of these assessment is that we provide strong evidences of the viability of MONEDA for handling hard and complex high-dimensional problems and its superior performance when compared to similar approaches. In spite of the fact that obviously further studies are necessary, these extensive experiments have provided solid ground for the use of MONEDA in more ambitious real-world applications.
  • C. Fonseca, X. Gandibleux, P. Korhonen, L. Martí, B. Naujoks, L. Thiele, J. Wallenius, and E. Zitzler, “Working Group on EMO for Interactive Multiobjective Optimization (1st Round),” in Hybrid and Robust Approaches to Multiobjective Optimization, Dagstuhl, Germany, 2009. bibtex url
    @inproceedings{marti-dag-2009,
    Address = {Dagstuhl, Germany},
    Author = {Fonseca, Carlos and Gandibleux, Xavier and Korhonen, Pekka and Mart\'{i}, Luis and Naujoks, Boris and Thiele, Lothar and Wallenius, Jyrki and Zitzler, Eckart},
    Booktitle = {Hybrid and Robust Approaches to Multiobjective Optimization},
    Date-Modified = {2010-12-22 23:25:07 +0100},
    Editor = {Deb, Kalyanmoy and Greco, Salvatore and Miettinen, Kaisa and Zitzler, Eckart},
    Issn = {1862-4405},
    Number = {09041},
    Publisher = {Schloss Dagstuhl --- Leibniz--Zentrum fuer Informatik},
    Series = {Dagstuhl Seminar Proceedings},
    Title = {{W}orking Group on {EMO} for Interactive Multiobjective Optimization (1st Round)},
    Url = {http://drops.dagstuhl.de/opus/volltexte/2009/2004},
    Year = {2009}}
  • L. Martí, J. García, A. Berlanga, and J. M. Molina, “Scalable Continuous Multiobjective Optimization with a Neural Network–Based Estimation of Distribution Algorithm,” in Applications of Evolutionary Computing, Heidelberg, 2008, pp. 535-544. doi: 10.1007/978-3-540-78761-7_59 bibtex abstract
    @inproceedings{marti-2008:evonum,
    Abstract = {To achieve a substantial improvement of MOEDAs regarding MOEAs it is necessary to adapt their model building algorithm to suit this particular task. Most current model building schemes used so far off-the-shelf machine learning methods. However, the model building problem has specific requirements that those methods do not meet and even avoid. In this we work propose a novel approach to model building in MOEDAs using an algorithm custom-made for the task. We base our proposal on the growing neural gas (GNG) network. The resulting model-building GNG (MB-GNG) is capable of yielding good results when confronted to high-dimensional problems.},
    Address = {Heidelberg},
    Author = {Mart\'{i}, Luis and Garc\'{i}a, Jes\'{u}s and Berlanga, Antonio and Molina, Jos\'{e} Manuel},
    Booktitle = {Applications of Evolutionary Computing},
    Doi = {10.1007/978-3-540-78761-7_59},
    Editor = {Giacobini, Mario and Brabazon, Anthony and Cagnoni, Stefano and Di Caro, Gianni~A. and Drechsler, Rolf and Ek\'{a}rt, Anik\'{o} and Esparcia-Alc\'{a}zar, Anna Isabel and Farooq, Muddassar and Fink, Andreas and McCormack, Jon and O'Neill, Michael and Romero, Juan and Rothlauf, Franz and Squillero, Giovanni and Uyar, A. \c{S}ima and Yang, Shengxiang},
    Isbn = {978-3-540-78760-0},
    Pages = {535--544},
    Publisher = {Springer},
    Series = {Lecture Notes in Computer Science},
    Title = {Scalable Continuous Multiobjective Optimization with a Neural Network--Based Estimation of Distribution Algorithm},
    Volume = {4974},
    Year = {2008}}
    To achieve a substantial improvement of MOEDAs regarding MOEAs it is necessary to adapt their model building algorithm to suit this particular task. Most current model building schemes used so far off-the-shelf machine learning methods. However, the model building problem has specific requirements that those methods do not meet and even avoid. In this we work propose a novel approach to model building in MOEDAs using an algorithm custom-made for the task. We base our proposal on the growing neural gas (GNG) network. The resulting model-building GNG (MB-GNG) is capable of yielding good results when confronted to high-dimensional problems.
  • L. Martí, J. García, A. Berlanga, and J. M. Molina, “Introducing MONEDA: Scalable Multiobjective Optimization with a Neural Estimation of Distribution Algorithm,” in GECCO’08: 10th Annual Conference on Genetic and Evolutionary Computation, New York, NY, USA, 2008, pp. 689-696. doi: 10.1145/1389095.1389230 bibtex abstract
    @inproceedings{marti-2008:moneda,
    Abstract = {In this paper we explore the model-building issue of multiobjective optimization estimation of distribution algorithms. We argue that model-building has some characteristics that differentiate it from other machine learning tasks. A novel algorithm called multiobjective neural estimation of distribution algorithm (MONEDA) is proposed to meet those characteristics. This algorithm uses a custom version of the growing neural gas (GNG) network specially meant for the model-building task. As part of this work, MONEDA is assessed with regard to other classical and state-of-the-art evolutionary multiobjective optimizers when solving some community accepted test problems.},
    Address = {New York, NY, USA},
    Author = {Mart\'{i}, Luis and Garc\'{i}a, Jes\'{u}s and Berlanga, Antonio and Molina, Jos\'{e} Manuel},
    Booktitle = {GECCO'08: 10th Annual Conference on Genetic and Evolutionary Computation},
    Date-Modified = {2010-12-22 23:59:25 +0100},
    Doi = {10.1145/1389095.1389230},
    Editor = {Keizer, Marten and Antoniol, Giulio and Congdon, Clare and Deb, Kalyanmoy and Doerr, Benjamin and Hansen, Nikolaus and Holmes, John and Hornby, Gergory and Howard, Daniel and Kennedy, John and Kumar, Sanjeev and Lobo, Ferdinando and Miller, Julian and Moore, Jason and Neumann, Frank and Pelikan, Martin and Pollack, Jordan and Sastry, Kumara and Stanley, Ken and Stoica, Adrian and Talbi, El Ghazli and Wegener, Ingo},
    Isbn = {978-1-60558-131-6},
    Location = {Atlanta (GA), USA},
    Note = {EMO Track ``Best Paper'' Nominee},
    Pages = {689--696},
    Publisher = {ACM Press},
    Title = {Introducing {MONEDA}: {S}calable Multiobjective Optimization with a Neural Estimation of Distribution Algorithm},
    Year = {2008}}
    In this paper we explore the model-building issue of multiobjective optimization estimation of distribution algorithms. We argue that model-building has some characteristics that differentiate it from other machine learning tasks. A novel algorithm called multiobjective neural estimation of distribution algorithm (MONEDA) is proposed to meet those characteristics. This algorithm uses a custom version of the growing neural gas (GNG) network specially meant for the model-building task. As part of this work, MONEDA is assessed with regard to other classical and state-of-the-art evolutionary multiobjective optimizers when solving some community accepted test problems.
  • L. Martí, J. García, A. Berlanga, and J. M. Molina, “Model-Building Algorithms for Multiobjective EDAs: Directions for Improvement,” in 2008 IEEE Conference on Evolutionary Computation (CEC), part of 2008 IEEE World Congress on Computational Intelligence (WCCI 2008), Piscataway, New Jersey, 2008, pp. 2848-2855. doi: 10.1109/CEC.2008.4631179 bibtex url
    @inproceedings{marti-2008:model-comp,
    Address = {Piscataway, New Jersey},
    Author = {Mart\'{i}, Luis and Garc\'{i}a, Jes\'{u}s and Berlanga, Antonio and Molina, Jos\'{e} Manuel},
    Booktitle = {2008 IEEE Conference on Evolutionary Computation (CEC), part of 2008 IEEE World Congress on Computational Intelligence (WCCI 2008)},
    Date-Modified = {2010-12-22 23:59:58 +0100},
    Doi = {10.1109/CEC.2008.4631179},
    Isbn = {978-1-4244-1823-7},
    Location = {Hong Kong, China},
    Pages = {2848--2855},
    Publisher = {IEEE Press},
    Title = {Model-Building Algorithms for Multiobjective {EDA}s: {D}irections for Improvement},
    Url = {http://ieeexplore.ieee.org/iel5/4625778/4630767/04631179.pdf?tp=&arnumber=4631179&isnumber=4630767},
    Year = {2008},
    Bdsk-Url-1 = {http://ieeexplore.ieee.org/iel5/4625778/4630767/04631179.pdf?tp=&arnumber=4631179&isnumber=4630767},
    Bdsk-Url-2 = {http://dx.doi.org/10.1109/CEC.2008.4631179}}
  • L. Martí, J. García, A. Berlanga, and J. M. Molina, “A Cumulative Evidential Stopping Criterion for Multiobjective Optimization Evolutionary Algorithms,” in GECCO’07: Proceedings of the 2007 GECCO Conference Companion on Genetic and Evolutionary Computation, New York, 2007, p. 911. doi: 10.1145/1276958.1277141 bibtex abstract url
    @inproceedings{marti-2007:gecco-stopping,
    Abstract = {In this work we present a novel and efficient algorithm independent stopping criterion, called the MGBM criterion,suitable for Multi-objective Optimization Evolutionary Algorithms (MOEAs).The criterion, after each iteration of the optimization algorithm, gathers evidence of the improvement of the solutions obtained so far. A global (execution wise) evidence accumulation process inspired by recursive Bayesian estimation decides when the optimization should be stopped. Evidence is collected using a novel relative improvement measure constructed on top of the Pareto dominance relations. The evidence gathered after each iteration is accumulated and updated following a rule based on a simplified version of a discrete Kalman filter.Our criterion is particularly useful in complex and/or high-dimensional problems where the traditional procedure of stopping after a predefined amount of iterations cannot be used and the waste of computational resources can induce to a detriment of the quality of the results.Although the criterion discussed here is meant for MOEAs,it can be easily adapted to other soft computing or numerical methods by substituting the local improvement metric with a suitable one.},
    Address = {New York},
    Author = {Mart\'{i}, Luis and Garc\'{i}a, Jes\'{u}s and Berlanga, Antonio and Molina, Jos\'{e} Manuel},
    Booktitle = {GECCO'07: Proceedings of the 2007 GECCO Conference Companion on Genetic and Evolutionary Computation},
    Doi = {10.1145/1276958.1277141},
    Editor = {Thierens, Dirk and Deb, Kalyanmoy and Pelikan, Martin and Beyer, Hans-Georg and Doerr, Benjamin and Poli, Riccardo and Bittari, Mauro},
    Isbn = {978-1-59593-697-4},
    Location = {London, UK},
    Pages = {911},
    Publisher = {ACM Press},
    Title = {A Cumulative Evidential Stopping Criterion for Multiobjective Optimization Evolutionary Algorithms},
    Url = {http://portal.acm.org/citation.cfm?doid=1276958.1277141},
    Year = {2007}}
    In this work we present a novel and efficient algorithm independent stopping criterion, called the MGBM criterion,suitable for Multi-objective Optimization Evolutionary Algorithms (MOEAs).The criterion, after each iteration of the optimization algorithm, gathers evidence of the improvement of the solutions obtained so far. A global (execution wise) evidence accumulation process inspired by recursive Bayesian estimation decides when the optimization should be stopped. Evidence is collected using a novel relative improvement measure constructed on top of the Pareto dominance relations. The evidence gathered after each iteration is accumulated and updated following a rule based on a simplified version of a discrete Kalman filter.Our criterion is particularly useful in complex and/or high-dimensional problems where the traditional procedure of stopping after a predefined amount of iterations cannot be used and the waste of computational resources can induce to a detriment of the quality of the results.Although the criterion discussed here is meant for MOEAs,it can be easily adapted to other soft computing or numerical methods by substituting the local improvement metric with a suitable one.
  • L. Martí, “A Hybrid Neural System based on Adaptive Resonance Theory and Representational Redescription capable of Variable Binding,” in 2007 International Joint Conference on Neural Networks (IJCNN), 2007, pp. 2448-2453. doi: 10.1109/IJCNN.2007.4371342 bibtex url
    @inproceedings{marti-2007:vabbiart,
    Author = {Mart\'{i}, Luis},
    Booktitle = {2007 International Joint Conference on Neural Networks (IJCNN)},
    Doi = {10.1109/IJCNN.2007.4371342},
    Editor = {Si, Jennie and Sun, Ron},
    Issn = {1098-7576},
    Pages = {2448--2453},
    Publisher = {IEEE Press},
    Title = {A Hybrid Neural System based on Adaptive Resonance Theory and Representational Redescription capable of Variable Binding},
    Url = {http://www.ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=4370891&arnumber=4371342&count=569&index=450},
    Year = {2007}}
  • L. Martí, J. García, A. Berlanga, and J. M. Molina, “A Cumulative Evidential Stopping Criterion for Multiobjective Optimization Evolutionary Algorithms (extended version),” in GECCO’07: Proceedings of the 2007 GECCO Conference Companion on Genetic and Evolutionary Computation, New York, NY, USA, 2007, pp. 2835-2842. doi: 10.1145/1274000.1274053 bibtex
    @inproceedings{marti-2007:gecco-stopping-extended,
    Address = {New York, NY, USA},
    Author = {Mart\'{i}, Luis and Garc\'{i}a, Jes\'{u}s and Berlanga, Antonio and Molina, Jos\'{e} Manuel},
    Booktitle = {GECCO'07: Proceedings of the 2007 GECCO Conference Companion on Genetic and Evolutionary Computation},
    Doi = {10.1145/1274000.1274053},
    Editor = {Thierens, Dirk and Deb, Kalyanmoy and Pelikan, Martin and Beyer, Hans-Georg and Doerr, Benjamin and Poli, Riccardo and Bittari, Mauro},
    Isbn = {978-1-59593-698-1},
    Location = {London, United Kingdom},
    Pages = {2835--2842},
    Publisher = {ACM},
    Title = {A Cumulative Evidential Stopping Criterion for Multiobjective Optimization Evolutionary Algorithms (extended version)},
    Year = {2007}}
  • L. Martí, A. Policriti, and L. García, “A Hybrid ART Neuro–fuzzy Architecture with Variable Binding,” in Proceedings of the First Cuban Artificial Intelligence Symposium, La Habana, Cuba, 2004. bibtex
    @inproceedings{marti-2004:hybrid-art,
    Address = {La Habana, Cuba},
    Author = {Mart\'i, Luis and Policriti, Alberto and Garc\'ia, Luciano},
    Booktitle = {Proceedings of the First Cuban Artificial Intelligence Symposium},
    Publisher = {Universidad de las Ciencias Inform\'aticas},
    Title = {A Hybrid {ART} Neuro--fuzzy Architecture with Variable Binding},
    Year = {2004}}
  • L. Martí, A. Policriti, and L. García, “Redes ART híbridas para la predicción de series de tiempo,” in Resúmenes del 8vo Congreso Nacional de Matemáticas y Computación (COMPUMAT’2003), 2003. bibtex
    @inproceedings{marti-2003:art-series,
    Author = {Mart\'i, Luis and Policriti, Alberto and Garc\'ia, Luciano},
    Booktitle = {Res\'{u}menes del 8vo Congreso Nacional de Matem\'aticas y Computaci\'on (COMPUMAT'2003)},
    Editor = {Garc\'{i}a, Mauro},
    Publisher = {Sociedad Cubana de Matem\'aticas y Computaci\'on},
    Title = {Redes {ART} h\'{i}bridas para la predicci\'on de series de tiempo},
    Year = {2003}}
  • L. Martí, A. Policriti, and L. García, “AppART: An ART Hybrid Stable Learning Neural Network for Universal Function Approximation,” in Hybrid Information Systems, Heidelberg, 2002, pp. 93-120. bibtex url
    @inproceedings{appart-his-2002,
    Address = {Heidelberg},
    Author = {Mart\'{i}, Luis and Policriti, Alberto and Garc\'{i}a, Luciano},
    Booktitle = {Hybrid Information Systems},
    Editor = {Abraham, Ajith and Koeppen, Mario},
    Pages = {93--120},
    Publisher = {Physica--Verlag},
    Title = {{AppART}: {A}n {ART} Hybrid Stable Learning Neural Network for Universal Function Approximation},
    Url = {http://www.springer.com/computer/artificial/book/978-3-7908-1480-4},
    Year = {2002}}
  • L. Martí, A. Policriti, L. García, and R. Lazo, “AppART + Growing Neural Gas = High Performance Hybrid Neural Network for Function Approximation,” in Workshop on Intelligent Knowledge Management Techniques (IKOMAT’2002), part of Knowledge-based Intelligent Information Engineering Systems & Allied Technologies (KES’2002), Amsterdam, 2002, pp. 1483-1487. bibtex url
    @inproceedings{Marti-GasART02,
    Address = {Amsterdam},
    Author = {Mart\'i, Luis and Policriti, Alberto and Garc\'ia, Luciano and Lazo, Raynel},
    Booktitle = {Workshop on Intelligent Knowledge Management Techniques (IKOMAT'2002), part of Knowledge-based Intelligent Information Engineering Systems \& Allied Technologies (KES'2002)},
    Date-Modified = {2009-04-30 15:56:22 +0200},
    Editor = {Bhattacharya, M. and Abraham, A.},
    Isbn = {978-1-58603-280-7},
    Pages = {1483--1487},
    Publisher = {IOS Press},
    Title = {{AppART} + Growing Neural Gas = High Performance Hybrid Neural Network for Function Approximation},
    Url = {http://www.iospress.nl/loadtop/load.php?isbn=9781586032807},
    Year = {2002},
    Bdsk-Url-1 = {http://www.iospress.nl/loadtop/load.php?isbn=9781586032807}}
  • L. Martí, M. Catasús, and L. García, “Artificial Neural Networks in Analytical Chemistry,” in CIMAF’99: International Conference Science and Technology for Development — Adaptive Systems Symposium, 1999. bibtex
    @inproceedings{marti-1999:cimaf,
    Author = {Luis Mart\'i and Miguel Catas\'us and Luciano Garc\'ia},
    Booktitle = {CIMAF'99: International Conference Science and Technology for Development --- Adaptive Systems Symposium},
    Title = {Artificial Neural Networks in Analytical Chemistry},
    Year = {1999}}

Technical Reports

  • L. Martí, J. García, A. Berlanga, and J. M. Molina, “MONEDA: Scalable Multi-Objective Optimization with a Neural Network-based Estimation of Distribution Algorithm,” Grupo de Inteligencia Artificial Aplicada, Universidad Carlos III de Madrid, Colmenarejo, Spain, GIAA2010E002, 2010. bibtex url
    @techreport{marti-2010:moneda-tr,
    Address = {Colmenarejo, Spain},
    Author = {Mart\'{i}, Luis and Garc\'{i}a, Jes\'{u}s and Berlanga, Antonio and Molina, Jos\'{e} Manuel},
    Institution = {Grupo de Inteligencia Artificial Aplicada, Universidad Carlos III de Madrid},
    Number = {GIAA2010E002},
    Title = {{MONEDA}: {S}calable Multi-Objective Optimization with a Neural Network-based Estimation of Distribution Algorithm},
    Url = {http://www.giaa.inf.uc3m.es/miembros/lmarti/moneda},
    Year = {2010}}
  • L. Martí, J. García, A. Berlanga, C. Coello Coello, and J. M. Molina, “On Current Model-Building Methods for Multi-Objective Estimation of Distribution Algorithms: Shortcommings and Directions for Improvement,” Grupo de Inteligencia Artificial Aplicada, Universidad Carlos III de Madrid, Colmenarejo, Spain, GIAA2010E001, 2010. bibtex url
    @techreport{marti-2010:model-building-tr,
    Address = {Colmenarejo, Spain},
    Author = {Mart\'{i}, Luis and Garc\'{i}a, Jes\'{u}s and Berlanga, Antonio and Coello Coello, Carlos~A. and Molina, Jos\'{e} Manuel},
    Date-Modified = {2010-12-22 23:46:11 +0100},
    Institution = {Grupo de Inteligencia Artificial Aplicada, Universidad Carlos III de Madrid},
    Number = {GIAA2010E001},
    Title = {On Current Model-Building Methods for Multi-Objective Estimation of Distribution Algorithms: Shortcommings and Directions for Improvement},
    Url = {http://www.giaa.inf.uc3m.es/miembros/lmarti/model-building},
    Year = {2010}}