The Variable Size Memory-based Evolutionary Algorithm in Dynamic Environments
Xiaofeng Yin · Journal of Northeastern University · 2011
Traditional memory-based evolutionary algorithms often may not achieve the desired performances in dynamic environments,which is mainly due to the fixed memory size.A variable size memory-based evolutionary algorithm is proposed.The improved memory enhanced evolutionary algorithm(IMEEA),which combines memory population and search population,and hyper-mutation is used to promote and maintain diversity.The two populations have minimum and maximum sizes allowed that change according to the stage of the evolutionary process.Simulation results show that the tracking error of the IMEEA is less than the memory enhanced evolutionary algorithm(MEEA),and then prove the effectiveness of this new algorithm.