Evolutionary optimization based on chaotic sequence in dynamic environments
Xiufen Zou, Wang Minling, Anmin Zhou, Bob McKay · 2004
In applications of the evolutionary algorithms (EAs) to problems of adaptation to changing environments, maintenance of the diversity of the population is an essential requirement. This paper proposes an evolutionary algorithm combined with a chaotic sequence (CEA) which provides a good technique for population diversity in dynamic optimization problems. Many numerical experiments are reported in order to compare the performance of the CEA with the self-adaptive approach by other authors, and the numerical results show that the performance of CEA algorithm is superior to that of other published algorithms for two dynamic benchmark problems.