Presentation 2000/5/19
A New Crossover Method for Distributed Genetic Algorithms
MIKI Mitsunori, HIROYASU Tomoyuki, YOSHIDA Jun-ichi, OHMUKAI Ikki,
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Abstract(in English) This paper proposes a new crossover method for distributed genetic algorithms(DGA). DGA with multiple Subpopulations provides better solutions than conventional GA with a single population, and the proposed method including the hybridization crossover and the best combinatorial crossover is developed to increase the performance of DGA. The proposed method provides high local search ability in each Subpopulation and high global search ability by the migration, and is evaluated with four standard test functions. The experimental results showed that the proposed method is very effective.
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Keyword(in English) Optimization / Evolutionary strategy / Genetic Algorithms / Distributed Populations / Crossover method
Paper # AI2000-15
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Committee AI
Conference Date 2000/5/19(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 Crossover Method for Distributed Genetic Algorithms
Sub Title (in English)
Keyword(1) Optimization
Keyword(2) Evolutionary strategy
Keyword(3) Genetic Algorithms
Keyword(4) Distributed Populations
Keyword(5) Crossover method
1st Author's Name MIKI Mitsunori
1st Author's Affiliation Knowledge Engineering Dept., Doshisha University()
2nd Author's Name HIROYASU Tomoyuki
2nd Author's Affiliation Knowledge Engineering Dept., Doshisha University
3rd Author's Name YOSHIDA Jun-ichi
3rd Author's Affiliation Graduate School of Engineering, Doshisha University
4th Author's Name OHMUKAI Ikki
4th Author's Affiliation Graduate School of Engineering, Doshisha University
Date 2000/5/19
Paper # AI2000-15
Volume (vol) vol.100
Number (no) 89
Page pp.pp.-
#Pages 8
Date of Issue