Improving performance of ethnic group evolution algorithm by Gray code

Abstract

The Ethnic Group Evolution Algorithm (EGEA) has used ethnic group mechanism, a kind of population-structured technology, to control the evolution tendency of population; meanwhile, it has used the binary code similarity among individuals to be the ethnic group clustering criterion. Because the hamming cliff problem of nature binary code was likely to affect the accuracy of ethnic group clustering, we proposed to make use of gray code to improve the evolution efficiency of EGEA. The simulations of numerical optimization show the EGEA based on gray code can improve the searching speed and the solution precision greatly.