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>>
Buy this Article




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.