A multiobjective methodology for evaluating genetic operators

Abstract—This paper is concerned with the problem of evalu-ating genetic algorithm (GA) operator combinations. Each GA op-erator, like crossover or mutation, can be implemented according to several different formulations. This paper shows that: 1) the performances of different operators are not independent and 2) different merit figures for measuring a GA performance are con-flicting. In order to account for this problem structure, a multiob-jective analysis methodology is proposed. This methodology is em-ployed 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. Index Terms—Genetic algorithm (GA), multiobjective perfor-mance evaluation. I