An adaptive genomic difference based genetic algorithm and its application to memetic continuous optimization

Continuous function optimization is ubiquitous in many branches of Science and Technology. Memetic algorithms are a particularly interesting approach to the optimization of continuous, non-linear, multimodal, ill-conditioned or noisy functions as these algorithms do not require derivatives and balance global exploratory search with local refinement. The Wang genetic algorithm promotes genetic diversity (exploratory capacities) by applying crossover only to parents with sufficient different chromosomes (genomes). In this work an improvement of the Wang algorithm is proposed that allows for an adaptive evaluation of the genomic difference between individuals in a way that is independent of the optimization problem and takes into account the stage of the evolutionary process. Moreover, the work proposes an original and relevant memetic algorithm combining the improved Wang genetic algorithm, for exploration purposes, with the covariance matrix adaptation evolutionary strategy (CMA-ES) for refinements. The proposed algorithm is empirically evaluated using 25 bench marking functions against five state-of-the-art memetic algorithms revealing superior performance which is a strong evidence on the relevance of proposed algorithm.