Abstract
Genetic algorithms (GAs) have achieved lots of success in various applications, but the problem to balance exploration and exploitation of population is still up in the air. In this paper, we propose a variant of genetic algorithm with mating operator GASF to alleviate the problem;GASFmeasures the mating attractiveness of individuals from four aspects: gender, age, similarity and fitness. Individuals are assigned gender to facilitate mimicking human reproduction, and contributions of age, similarity and fitness to the attractiveness are respectively quantified and self-adaptively adjusted. Experimental results indicate that the proposed approach can achieve competitive performance with improved convergence.
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