Computational geometry: algorithms and applications
Computational geometry: algorithms and applications
Parameter Setting in Evolutionary Algorithms
Parameter Setting in Evolutionary Algorithms
A genetic algorithm that adaptively mutates and never revisits
IEEE Transactions on Evolutionary Computation
Continuous non-revisiting genetic algorithm
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
Continuous non-revisiting genetic algorithm
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
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We study empirically the effects of operator and parameter choices on the performance of the nonrevisiting genetic algorithm (NrGA). For a suite of 14 benchmark functions that include both uni-modal and multimodal 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.