self-adaptive mutation in the differential evolution
The Differential Evolution (DE) algorithm is an efficient and powerful evolutionary algorithm (EA) for solving optimization problems. However the success of DE in solving a specific problem is closely related to appropriately choosing its control parameters. Parameter tuning leads to additional computational costs because of time-consuming trial-and-error tests. Self-adaptation, in contrast, allows the algorithm to reconfigure itself, automatically adapting to the problem being solved. There are in the literature some self-adaptive versions of differential evolution, however they do not align completely with self-adaptation concepts. In this paper, some self-adaptive versions of DE in the literature are described and discussed, and then a new Self-Adaptive Differential Evolution with multiple mutation strategies is proposed; it is called Self-adaptive Mutation Differential Evolution (SaMDE) and aims at preserving the essential characteristics of self-adaptation. Some computational experiments which illustrate algorithm behaviour and a comparative test with the classical DE and with an important self-adaptive DE are presented. The results suggest that SaMDE is a very promising algorithm.
self-adaptation differential evolution evolutionary algorithms numerical optimization