An Effective Combination of Genetic Operators in Evolutionary Algorithm

An evolutionary algorithm (EA) is designed and then is used to solve constrained optimization problems in this paper. The difference of the proposed algorithm from other EAs stays in combination of two crossover operators: one is affine crossover which inherits characteristics of the parents by using function continuity, one is uniform crossover which preserves some discrete genes of the parents by using Darwin's principle. Since both crossovers are independent to some extent, population diversity could be well maintained, then the new EA (denoted FUXEA) could enhance capacity in global search. The FUXEA algorithm is compared with some state-of-the-art algorithms which were published in a best journal in evolutionary computation area, and 13 widely used constraint benchmark problems to test the algorithm. The experimental results suggest it outperforms to or not worse than others, especially for the problems with many local optima, it performs much better.

I. Introduction

Evolutionary algorithms (EAs) have been widely studied and used to solve optimization problems especially complicated problems, including dealing with constraint optimization problems (COPs).