Presentation 2004-06-21
A new selection method in a genetic algorithm : Theory
Tetsuaki HIRAHAYA, Satoshi SAWATANI, Yasuko MUNEHISA, Tomo MUNEHISA,
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Abstract(in English) Genetic algorithms have the fault that the optimal solution attainment is not guaranteed. In order to guarantee the convergence, there is an approach which applies simulated anealings to genetic algorithms. However, applying this method to genetic algorithms breaks the guarantee. We propose a new selection method in order to guarantee the convergence by the on-off probability function.
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Keyword(in English) Genetic algorithms / Markov chain / Boltzmann distribution
Paper # AI2004-9
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Committee AI
Conference Date 2004/6/14(1days)
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Registration To Artificial Intelligence and Knowledge-Based Processing (AI)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) A new selection method in a genetic algorithm : Theory
Sub Title (in English)
Keyword(1) Genetic algorithms
Keyword(2) Markov chain
Keyword(3) Boltzmann distribution
1st Author's Name Tetsuaki HIRAHAYA
1st Author's Affiliation Faculty of Engineering, Univ. Yamanashi()
2nd Author's Name Satoshi SAWATANI
2nd Author's Affiliation Faculty of Engineering, Univ. Yamanashi
3rd Author's Name Yasuko MUNEHISA
3rd Author's Affiliation Faculty of Engineering, Univ. Yamanashi
4th Author's Name Tomo MUNEHISA
4th Author's Affiliation Faculty of Engineering, Univ. Yamanashi
Date 2004-06-21
Paper # AI2004-9
Volume (vol) vol.104
Number (no) 133
Page pp.pp.-
#Pages 6
Date of Issue