Presentation 2003/1/28
A Study on Analyses of Genetic Algorithms by using Mixture Models
Jun-ichi IMAI, Hiroyuki SHIOYA, Masahito KUIRIHARA,
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Abstract(in English) Some mathematical models have been proposed for theoretical analyses of genetic algorithms (GAs) . However, these works have limited their objects to a few kinds of GAs in order to formulate them accurately. In this paper, we regard a GA as an information source that generates input-output data. That is, we regard a population and its next population generated by the GA as input and output respectively. Then we model the GA by learning from these data. By using this method, we can describe a variety of GAs in a common form, and analyze them from a new point of view. We use some mixture models for modeling GAs in this paper. By using a mixture model, we can represent the GA system as a combination of some partial systems. In this paper, we treat two types of mixture models, and investigate how these models are effective for analyzing GAs through some experiments.
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Keyword(in English) Genetic Algorithm / Mixture Model / Vector Field / Modeling by Learning
Paper # NC2002-128
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Committee NC
Conference Date 2003/1/28(1days)
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Registration To Neurocomputing (NC)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) A Study on Analyses of Genetic Algorithms by using Mixture Models
Sub Title (in English)
Keyword(1) Genetic Algorithm
Keyword(2) Mixture Model
Keyword(3) Vector Field
Keyword(4) Modeling by Learning
1st Author's Name Jun-ichi IMAI
1st Author's Affiliation Graduate School of Engineering, Hokkaido University()
2nd Author's Name Hiroyuki SHIOYA
2nd Author's Affiliation Faculty of Engineering, Muroran Institute of Technology
3rd Author's Name Masahito KUIRIHARA
3rd Author's Affiliation Graduate School of Engineering, Hokkaido University
Date 2003/1/28
Paper # NC2002-128
Volume (vol) vol.102
Number (no) 628
Page pp.pp.-
#Pages 6
Date of Issue