Enhancing the Performance of Evolutionary Algorithms: a Novel Maturity-Based Adaptation Strategy

Abstract

Adapting genetic operators and parameter settings during the optimization process can improve the overall performance of evolutionary algorithms (EAs). In this paper, a novel maturity-based adaptation strategy for EAs is proposed. During the search process, a maturity degree of the population is calculated based on both the population distribution in the search space and that in the fitness space. According to the maturity degree, four evolution states of EAs are defined by a set of thresholds. As both the convergence of the genotypes (reflected by the geographical distance among the individuals) and that of the phenotypes (shown by the differentiation over individuals' fitness values) are taken into consideration, the estimation of the evolution state is very comprehensive. Then, a set of adaptation rules is applied to adapt the parameters and operators of EAs according to the maturity degree and evolution state. Implemented on genetic algorithm (GA), the probabilities of crossover and mutation are tuned to fulfill the current evolution requirement of the population. Meanwhile, a novel allele gene-based mutation scheme and the traditional mutation are alternately executed. Experimental results on eight benchmark functions show that the proposed maturity-based adaptation strategy can bring significant improvements in search speed, solution accuracy and robustness.