Globally Multimodal Problem Optimization Via an Estimation of Distribution Algorithm Based on Unsupervised Learning of Bayesian Networks

Authors:
J. M. Peña;J. A. Lozano;P. Larrañaga
Affiliations:
Computational Biology, Dept. of Physics and Measurement Technology, Linköping University, Sweden;Intelligent Systems Group, Dept. of Computer Science and Artificial Intelligence, University of the Basque Country, Spain;Intelligent Systems Group, Dept. of Computer Science and Artificial Intelligence, University of the Basque Country, Spain
Venue:
Evolutionary Computation
Year:
2005

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Abstract

Many optimization problems are what can be called globally multimodal, i.e., they present several global optima. Unfortunately, this is a major source of difficulties for most estimation of distribution algorithms, making their effectiveness and efficiency degrade, due to genetic drift. With the aim of overcoming these drawbacks for discrete globally multimodal problem optimization, this paper introduces and evaluates a new estimation of distribution algorithm based on unsupervised learning of Bayesian networks. We report the satisfactory results of our experiments with symmetrical binary optimization problems.