Associative memory scheme for genetic algorithms in dynamic environments

In recent years dynamic optimization problems have attracted a growing interest from the community of genetic algorithms with several approaches developed to address these problems, of which the memory scheme is a major one. In this paper an associative memory scheme is proposed for genetic algorithms to enhance their performance in dynamic environments. In this memory scheme, the environmental information is also stored and associated with current best individual of the population in the memory. When the environment changes the stored environmental information that is associated with the best re-evaluated memory solution is extracted to create new individuals into the population. Based on a series of systematically constructed dynamic test environments, experiments are carried out to validate the proposed associative memory scheme. The environmental results show the efficiency of the associative memory scheme for genetic algorithms in dynamic environments.