An improved evolutionary programming for optimization

To avoid premature convergence and balance the exploration and exploitation abilities of classic evolutionary programming, this paper proposes an improved evolutionary programming for optimization. Firstly, multiple populations are designed to perform parallel search with random initialization in divided solution spaces. Secondly, multiple mutation operators are designed to enhance the search templates. Thirdly, selection with probabilistic updating strategy based on annealing schedule like simulated annealing is applied to avoid the dependence on fitness function and to avoid being trapped in local optimum. Lastly, re-assignment strategy for individuals is designed for every sub-population to fuse information and enhance population diversity. Furthermore, the implementations of the proposed algorithm for function and combinatorial optimization problems are discussed and its effectiveness is demonstrated by numerical simulation based on some benchmarks.