A Genetic Algorithm with Conditional Crossover and Mutation Operators and Its Application to Combinatorial Optimization Problems(Neural Networks and Bioengineering)
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概要
- 論文の詳細を見る
In this paper, we present a modified genetic algorithm for solving combinatorial optimization problems. The modified genetic algorithm in which crossover and mutation are performed conditionally instead of probabilistically has higher global and local search ability and is more easily applied to a problem than the conventional genetic algorithms. Three optimization problems are used to test the performances of the modified genetic algorithm. Experimental studies show that the modified genetic algorithm produces better results over the conventional one and other methods.
- 社団法人電子情報通信学会の論文
- 2007-01-01
著者
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WANG Rong-Long
Faculty of Engineering, Fukui University
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Wang Rong-long
Faculty Of Engineering Toyama University
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Wang Rong-long
The Authors Are With The Faculty Of Engineering University Of Fukui
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Wang Rong-long
Faculty Of Engineering University Of Fukui
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OKAZAKI Kozo
Faculty of Engineering, University of Fukui
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Okazaki Kozo
Faculty Of Engineering University Of Fukui
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FUKUTA Shinichi
Faculty of Engineering, University of Fukui
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WANG Jia-Hai
Department of Computer Science, Sun Yatsen University
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Wang Jia-hai
Department Of Computer Science Sun Yatsen University
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Fukuta Shinichi
Faculty Of Engineering University Of Fukui
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Wang Rong-long
Faculty Of Engineering Fukui University
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