Analysis of evolutionary process using evolutionary activity and modular schema analysis

A genetic algorithm is developed to find an optimal solution in a large search space using selection, crossover, and mutation. Some researchers have studied techniques for analysis of evolution process in genetic algorithm. In most cases, they were applied to only simple problem or they used schema theorem and numerical statistics to examine the process. These techniques are mostly developed because tracing schemas and interpreting semantics of the schemas require much effort and time. In this paper, we propose modular encoding of gene, which is used to facilitate the interpretation of the gene, identification of important parts of genetic code using evolutionary activity statistics, and analysis of the schemas. Also, we show the feasibility of the proposed method by tracing the evolution process of fuzzy robot controller.