Asynchronous differential evolution with adaptive correlation matrix

Differential evolution (DE) is an efficient algorithm to solve global optimization problems. It has a simple internal structure and uses a few control parameters. In this paper we incorporate crossover based on adaptive correlation matrix into Asynchronous differential evolution (ADE). Thanks to the proposed crossover the novel algorithm automatically adapts to the landscape of the optimized objective function. Combined with an adaptive scheme for the mutation scale factor and an automatic inflation of the population size this results in quasi parameter-free algorithm from the user's point of view. The performance of the Asynchronous differential evolution with adaptive correlation matrix is reported on the set of BBOB-2012 benchmark functions.