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A Genetic Algorithm with Conditional Crossover and Mutation Operators and Its Application to Combinatorial Optimization Problems Rong-Long WANG Shinichi FUKUTA Jia-Hai WANG Kozo OKAZAKI Publication IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences Vol.E90-A No.1 pp.287-294 Publication Date: 2007/01/01 Online ISSN: 1745-1337 DOI: 10.1093/ietfec/e90-a.1.287 Print ISSN: 0916-8508 Type of Manuscript: PAPER Category: Neural Networks and Bioengineering Keyword: genetic algorithm, combinatorial optimization problem, subset sum problem, set-covering problem, Full Text: PDF(470.8KB)>>
Summary: 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. | ||||||||||
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