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Globally multimodal problem optimization via an estimation of distribution algorithm based on unsupervised learning of Bayesian networks
Linköping University, Department of Computer and Information Science, Database and information techniques. (ADIT)
University of the Basque Country, Spain.
University of the Basque Country, Spain.
2005 (English)In: Evolutionary Computation, ISSN 1063-6560, E-ISSN 1530-9304, Vol. 13, no 1, p. 43-66Article in journal (Refereed) Published
Abstract [en]

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. © 2005 by the Massachusetts Institute of Technology.

Place, publisher, year, edition, pages
2005. Vol. 13, no 1, p. 43-66
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Natural Sciences
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URN: urn:nbn:se:liu:diva-30219DOI: 10.1162/1063656053583432Local ID: 15716OAI: oai:DiVA.org:liu-30219DiVA, id: diva2:251041
Available from: 2009-10-09 Created: 2009-10-09 Last updated: 2017-12-13

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Peña, Jose M.

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