NDLI: Estimation of distribution algorithms with niche separation mechanism

Content Provider	IEEE Xplore Digital Library
Author	Handa, H.
Copyright Year	2007
Description	Author affiliation: Okayama Univ., Okayama (Handa, H.)
Abstract	Estimation of distribution algorithms are a promising method in evolutionary computation. In the estimation of distribution algorithms, instead of using conventional crossover and mutation operations, probabilistic models are used to sample the genetic information in the next population. Although the use of probabilistic models enables EDAs to have several schema simultaneously, it sometimes causes worse performance due to converse schemata. In this paper, estimation of distribution algorithms with niche separation mechanism is proposed. Coexistence schemata are also newly introduced, where schemata at the same loci but converse information. The proposed method splits population into two subpopulations if such coexistence schemata are found. One of subpopulations is used to constitute the next generation while the probabilistic model of another subpopulation is enqueued. The queued probabilistic model is used if the current population is converged. The experimental results on max-sat problems and Ising spin glass problems show the effectiveness of the proposed method.
Starting Page	119
Ending Page	126
File Size	172357
Page Count	8
File Format	PDF
ISBN	9781424413393
DOI	10.1109/CEC.2007.4424462
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-09-25
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Electronic design automation and methodology Evolutionary computation Genetic mutations Glass Data mining Computational modeling Character generation Sampling methods Mutual information Genetic algorithms
Content Type	Text
Resource Type	Article

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