Parameter selection in genetic algorithms
Journal of Systemics, Cybernetics and Informatics
1690-4532 (print)1690-4524 (online)
International Institute of Informatics and Cybernetics
78 - 83
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Please cite this item using this persistent URLhttp://hdl.handle.net/11693/49280
In this study, we provide a new taxonomy of parameters of genetic algorithms (GA), structural and numerical parameters, and analyze the effect of numerical parameters on the performance of GA based simulation optimization applications with experimental design techniques. Appropriate levels of each parameter are proposed for a particular problem domain. Controversial to existing literature on GA, our computational results reveal that in the case of a dominant set of decision variable the crossover operator does not have a significant impact on the performance measures, whereas high mutation rates are more suitable for GA applications.