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 afflne 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 Darwins 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.
Evolutionary algorithm Genetic operator Constrained optimization
Qing Zhang Sanyou Zeng Zhengjun Li Hongyong Jing
School of Mathematics&Computer Science Huanggang Normal University 438000, Huanggang, Hubei,P.R. Chi School of Computer Science China University of GeoSciences 430074 Wuhan, Hubei, P.R.China Antenna Research Center China Academy of Space Technology 710000 Xian, Shanxi, P.R.China
国际会议
杭州
英文
105-109
2011-10-28（万方平台首次上网日期，不代表论文的发表时间）