chaotic genetic algorithm based on explicit memory with a new strategy for updating and retrieval of memory in dynamic environments

Many problems considered in the optimization and learning processes assume that solutions change dynamically. hence, the algorithms are required that dynamically adapt with the new conditions of the problem through searching new conditions. mostly, utilization of information from the past allows to quickly adapting changes right after they occur in the environment. this is the idea underlining the use of memory in this field, what involves the key design issues concerning the memory content, update process, and retrieval process. in this work, we use the chaotic genetic algorithm (ga) with memory for solving dynamic optimization problems. a chaotic system has a much more accurate prediction of the future compared with a random system. the proposed method uses a new memory with diversity maximization. here, we propose a new strategy for updating memory and memory retrieval. an experimental study is conducted based on the moving peaks benchmark (mpb) in order to test the performance of the developed method in comparison with several state-of-the-art algorithms from the literature. the experimental results obtained show the superiority and more effectiveness of the proposed algorithm in dynamic environments.