A new mutation rule for evolutionary programming motivated from backpropagation learning

Abstract

Evolutionary programming is mainly characterized by two factors: the selection strategy and the mutation rule. This letter presents a new mutation rule that has the same form as the well-known backpropagation learning rule for neural networks. The proposed mutation rule assigns the best individual's fitness as the temporary target at each generation. The temporal error, the distance between the target and an individual at hand, is used to improve the exploration of the search space by guiding the direction of evolution. The momentum, i,e,, the accumulated evolution information for the individual, speeds up convergence. The efficiency and robustness of the proposed algorithm are assessed on several benchmark test functions.