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A Multiobjective Methodology for Evaluating Genetic Operators

Abstract : This paper is concerned with the problem of evaluating genetic algorithm (GA) operator combinations. Each GA operator, like crossover or mutation, can be implemented according to several different formulations. This paper shows that: 1) the performances of different operators are not dependent and 2) different merit figures for measuring a GA performance are conflicting. In order to account for this problem structure, a multiobjective analysis methodology is proposed. This methodology is employed for the evaluation of a new crossover operator (real-biased crossover) that is shown to bring a performance enhancement. A GA that was found by the proposed methodology is applied in an electromagnetic (EM) benchmark problem.
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Submitted on : Tuesday, October 31, 2006 - 5:07:23 PM
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Ricardo Takahashi, Joao Vasconcelos, Jaime Ramirez, Laurent Krähenbühl. A Multiobjective Methodology for Evaluating Genetic Operators. IEEE Transactions on Magnetics, Institute of Electrical and Electronics Engineers, 2003, 39 (3), pp.1321-1324. ⟨10.1109/TMAG.2003.810371⟩. ⟨hal-00082764⟩



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