Genetic Algorithms, Efficiency Enhancement, And Deciding Well With Differing Fitness Variances
نویسندگان
چکیده
This study investigates the decision making between fitness function with differing variance and computational-cost values. The objective of this decision making is to provide evaluation relaxation and thus enhance the efficiency of the genetic search. A decisionmaking strategy has been developed to maximize speed-up using facetwise models for the convergence time and population sizing. Results indicate that using this decision making, significant speed-up can be obtained.
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