An improved genetic algorithm with conditional genetic operators and its application to set-covering problem |
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Authors: | Rong-Long Wang Kozo Okazaki |
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Affiliation: | (1) Faculty of Engineering, Fukui University, Fukui-shi 910-8507, Japan |
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Abstract: | The genetic algorithm (GA) is a popular, biologically inspired optimization method. However, in the GA there is no rule of
thumb to design the GA operators and select GA parameters. Instead, trial-and-error has to be applied. In this paper we present
an improved genetic algorithm in which crossover and mutation are performed conditionally instead of probability. Because
there are no crossover rate and mutation rate to be selected, the proposed improved GA can be more easily applied to a problem
than the conventional genetic algorithms. The proposed improved genetic algorithm is applied to solve the set-covering problem.
Experimental studies show that the improved GA produces better results over the conventional one and other methods. |
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Keywords: | Genetic algorithm Genetic operator Combinatorial optimization Set-covering problem |
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