Novel Associative Memory Retrieving Strategies for Evolutionary Algorithms in Dynamic Environments

Recently, Evolutionary Algorithms (EAs) with associative memory schemes have been developed to solve Dynamic Optimization Problems (DOPs). Current associative memory schemes always retrieve both the best memory individual and the corresponding environmental information. However, the memory individual with the best fitness could not be the most appropriate one for new environments. In this paper, two novel associative memory retrieving strategies are proposed to obtain the most appropriate memory environmental information. In these strategies, two best individuals are first selected from the two best memory individuals and the current best individual. Then, their corresponding environmental information is evaluated according to either the survivability or the diversity, one of which is retrieved. In experiments, the proposed two strategies were embedded into the state-of-the-art algorithm, i.e. the MPBIL, and tested on three dynamic functions in cyclic environments. Experiment results demonstrate that the proposed retrieving strategies enhance the search ability in cyclic environments.