Self-adaptive Asynchronous Differential Evolution

Differential Evolution (DE) is a popular and efficient continuous optimization technique based on the principles of Darwinian evolution. In a previous work we have introduced Asynchronous Differential Evolution (ADE), a DE generalization that allows to regulate the synchronization mechanism of the algorithm by tuning two additional parameters. However, it is well known that an evolutionary algorithm must have a small number of parameters in order to be easy to use. Moving from this consideration, in this paper we introduce two different selfadaptive ADE schemes built on top of jDE-2, one of the most efficient self-adaptive DE variant. Experiments on well known benchmark functions have been held and the performances of the proposed SaADE schemes are compared with those of jDE- 2.