A study of operator and parameter choices in non-revisiting genetic algorithm

We study empirically the effects of operator and parameter choices on the performance of the non-revisiting genetic algorithm (NrGA). For a suite of 14 benchmark functions that include both uni-modal and multi-modal functions, it is found that NrGA is insensitive to the axis resolution of the problem, which is a good feature. From the empirical experiments, for operators, it is found that crossover is an essential operator for NrGA, and the best crossover operator is uniform crossover, while the best selection operator is elitist selection. For parameters, a small population, with a population size strictly larger than 1, should be used; the performance is monotonically increasing with crossover rate and the optimal crossover rate is 0.5. The results of this paper provide empirical guidelines for operator designs and parameter settings of NrGA.