Intelligent transport systems based on diversity growing using evolutionary parallel computation

Abstract

This paper proposes the generation of diverse intelligence based on a chaotic evolutionary computation algorithm instead of conventional GA (genetic algorithm) for such mobile-agent systems as intelligent transport systems (ITS). This evolutionary computation is realized by applying chaotic retrieval and soft DNA (soft computing oriented data driven functional scheduling architecture) on associative memories. We apply this evolutionary computation to multiagent systems such as ITS. Essentially, the process of this evolutionary computation is parallel processing. Therefore, we also propose its parallel processing algorithm. We show the usefulness of the proposed method by means of simulation experiment.