Adaptive k-tournament mutation scheme for differential evolution

Mutation in differential evolution (DE) is of considerable importance for the performance of the algorithm. It directly impacts exploration and exploitation. Thus, it represents the driving force for discovering unvisited regions of the search space, whilst also enabling the utilisation of promising points in that space. Since mutation performs search around the base vector, its selection plays a prominent role in directing it. In that regard, a low selection pressure contributes to exploration, whereas a high selection pressure contributes to exploitation. However, a balance between the two is paramount for high and consistent performance. This paper proposes a novel mutation scheme that employs k-tournament selection for choosing the base vector. Each population member is associated with a tournament size that is adapted during the search process with the aim of controlling exploration and exploitation. The mechanism mixes adaptation on an individual and population level. Results of the experimental analysis conducted on a wide range of numerical benchmark problem instances affirm its competitive performance and the benefits of the adaptation of tournament sizes, suggesting it to be a viable measure for increasing DE algorithm performance. Finally, the automatic design of radial basis function networks for classification was tackled. The proposed mutation scheme proved to be effective when dealing with that task as the canonical algorithm incorporating it yielded better fit models than competing approaches. •A novel mutation scheme for differential evolution is proposed.•The mutation scheme is based on adaptive k-tournaments.•Tournament sizes are adapted on a population and member level.•Results suggest competitive performance and benefits of the adaptation mechanism.•Results on the problem of automatic RBFN design for classification affirm its utility.