Alopex-based mutation strategy in Differential Evolution

Differential Evolution represents a class of evolutionary algorithms that are highly competitive for solving numerical optimization problems. In a Differential Evolution algorithm, there are a few alternative mutation strategies, which may lead to good or a bad performance depending on the property of the problem. A new mutation strategy, called DE/Alopex/1, is proposed in this paper. This mutation strategy distinguishes itself from other mutation strategies in that it uses the fitness values of the individuals in the population in order to calculate the probabilities of move directions. The performance of DE/Alopex/1 has been evaluated on the benchmark suite from CEC2013. The results of the experiments show that DE/Alopex/1 outperforms some state-of-the-art mutation strategies.