Critical Issues in Model-Based Surrogate Functions in Estimation of Distribution Algorithms

In many optimization domains the solution of the problem can be made more efficient by the construction of a surrogate fitness model. Estimation of distribution algorithms (EDAs) are a class of evolutionary algorithms particularly suitable for the conception of model-based surrogate techniques. Since EDAs generate probabilistic models, it is natural to use these models as surrogates. However, there exist many types of models and methods to learn them. The issues involved in the conception of model-based surrogates for EDAs are various and some of them have received scarce attention in the literature. In this position paper, we propose a unified view for model-based surrogates in EDAs and identify a number of critical issues that should be dealt with in order to advance the research in this area.

[1]  Arturo Hernández-Aguirre,et al.  Approximating the search distribution to the selection distribution in EDAs , 2009, GECCO 2009.

[2]  L. Kallel,et al.  Theoretical Aspects of Evolutionary Computing , 2001, Natural Computing Series.

[3]  Qingfu Zhang,et al.  MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition , 2007, IEEE Transactions on Evolutionary Computation.

[4]  Carlos A. Coello Coello,et al.  GIAA TECHNINAL REPORT GIAA2010E001 On Current Model-Building Methods for Multi-Objective Estimation of Distribution Algorithms: Shortcommings and Directions for Improvement , 2010 .

[5]  Siddhartha Shakya,et al.  Markov Networks in Evolutionary Computation , 2012 .

[6]  Siddhartha Shakya,et al.  Using a Markov network model in a univariate EDA: an empirical cost-benefit analysis , 2005, GECCO '05.

[7]  Khaled Rasheed,et al.  A Survey of Fitness Approximation Methods Applied in Evolutionary Algorithms , 2010 .

[8]  Gary B. Lamont,et al.  Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation) , 2006 .

[9]  Masaharu Munetomo,et al.  Introducing assignment functions to Bayesian optimization algorithms , 2008, Inf. Sci..

[10]  Alexander Mendiburu,et al.  The Plackett-Luce ranking model on permutation-based optimization problems , 2013, 2013 IEEE Congress on Evolutionary Computation.

[11]  David E. Goldberg,et al.  Multiobjective Estimation of Distribution Algorithms , 2006, Scalable Optimization via Probabilistic Modeling.

[12]  Siddhartha Shakya,et al.  The Markov network fitness model , 2012 .

[13]  Roberto Santana,et al.  Toward Understanding EDAs Based on Bayesian Networks Through a Quantitative Analysis , 2012, IEEE Transactions on Evolutionary Computation.

[14]  Pedro Larrañaga,et al.  Estimation of Distribution Algorithms , 2002, Genetic Algorithms and Evolutionary Computation.

[15]  Roberto Battiti,et al.  Active Learning of Pareto Fronts , 2014, IEEE Transactions on Neural Networks and Learning Systems.

[16]  Gary B. Lamont,et al.  Evolutionary Algorithms for Solving Multi-Objective Problems , 2002, Genetic Algorithms and Evolutionary Computation.

[17]  Concha Bielza,et al.  Network measures for information extraction in evolutionary algorithms , 2013, Int. J. Comput. Intell. Syst..

[18]  Jacek M. Zurada,et al.  Swarm and Evolutionary Computation , 2012, Lecture Notes in Computer Science.

[19]  Martin Pelikan,et al.  Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications (Studies in Computational Intelligence) , 2006 .

[20]  Ernesto Costa,et al.  Too Busy to Learn , 2000 .

[21]  Concha Bielza,et al.  Multi-objective Optimization with Joint Probabilistic Modeling of Objectives and Variables , 2011, EMO.

[22]  J. A. Lozano,et al.  Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation , 2001 .

[23]  Pedro Larrañaga,et al.  Evolutionary computation based on Bayesian classifiers , 2004 .

[24]  John A. W. McCall,et al.  An application of a GA with Markov network surrogate to feature selection , 2013, Int. J. Syst. Sci..

[25]  Yaochu Jin,et al.  Surrogate-assisted evolutionary computation: Recent advances and future challenges , 2011, Swarm Evol. Comput..

[26]  Concha Bielza,et al.  Mateda-2.0: A MATLAB package for the implementation and analysis of estimation of distribution algorithms , 2010 .

[27]  J. A. Lozano,et al.  Analyzing the k Most Probable Solutions in EDAs Based on Bayesian Networks , 2010 .

[28]  J. A. Lozano,et al.  Towards a New Evolutionary Computation: Advances on Estimation of Distribution Algorithms (Studies in Fuzziness and Soft Computing) , 2006 .

[29]  David E. Goldberg,et al.  Efficiency enhancement of genetic algorithms via building-block-wise fitness estimation , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[30]  Concha Bielza,et al.  Quantitative genetics in multi-objective optimization algorithms: from useful insights to effective methods , 2011, GECCO '11.

[31]  T. Mahnig,et al.  Evolutionary algorithms: from recombination to search distributions , 2001 .

[32]  Linda C. van der Gaag,et al.  Inference and Learning in Multi-dimensional Bayesian Network Classifiers , 2007, ECSQARU.

[33]  Martin V. Butz,et al.  Automated Global Structure Extraction for Effective Local Building Block Processing in XCS , 2006, Evolutionary Computation.

[34]  Qingfu Zhang,et al.  Approximating the Set of Pareto-Optimal Solutions in Both the Decision and Objective Spaces by an Estimation of Distribution Algorithm , 2009, IEEE Transactions on Evolutionary Computation.

[35]  Michael Clarke,et al.  Symbolic and Quantitative Approaches to Reasoning and Uncertainty , 1991, Lecture Notes in Computer Science.

[36]  Eckart Zitzler,et al.  Pattern identification in pareto-set approximations , 2008, GECCO '08.

[37]  David E. Goldberg,et al.  Substructrual surrogates for learning decomposable classification problems: implementation and first results , 2007, GECCO '07.

[38]  Peter A. N. Bosman,et al.  Design and Application of iterated Density-Estimation Evolutionary Algorithms , 2003 .

[39]  Concha Bielza,et al.  Continuous Estimation of Distribution Algorithms Based on Factorized Gaussian Markov Networks , 2012 .

[40]  Alexander Mendiburu,et al.  A review on estimation of distribution algorithms in permutation-based combinatorial optimization problems , 2012, Progress in Artificial Intelligence.

[41]  Andreas Krause,et al.  Active Learning for Multi-Objective Optimization , 2013, ICML.

[42]  Concha Bielza,et al.  Multi-dimensional classification with Bayesian networks , 2011, Int. J. Approx. Reason..

[43]  Concha Bielza,et al.  A review on probabilistic graphical models in evolutionary computation , 2012, Journal of Heuristics.

[44]  Pedro Larrañaga,et al.  Towards a New Evolutionary Computation - Advances in the Estimation of Distribution Algorithms , 2006, Towards a New Evolutionary Computation.

[45]  Dirk Thierens,et al.  Multi-objective optimization with diversity preserving mixture-based iterated density estimation evolutionary algorithms , 2002, Int. J. Approx. Reason..

[46]  Olivier Regnier-Coudert,et al.  Bayesian network structure learning using characteristic properties of permutation representations with applications to prostate cancer treatment , 2013 .

[47]  Carlos A. Coello Coello,et al.  Objective reduction using a feature selection technique , 2008, GECCO '08.

[48]  Qingfu Zhang,et al.  Approaches to selection and their effect on fitness modelling in an Estimation of Distribution Algorithm , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[49]  Siddhartha Shakya,et al.  Probabilistic Graphical Models and Markov Networks , 2012 .