A Selection Scheme for Excluding Defective Rules of Evolutionary Fuzzy Path Planning

This paper proposes a new selection mechanism in evolutionary algorithm for fuzzy systems that can be applied to robot learning of shooting ability in robot soccer. In generic evolutionary algorithms, evaluation and selection are performed on the chromosome level, where a selected chromosome may include non-effective or bad genes. This may lead to an increase in the uncertainty of the solutions. To solve this problem, we propose a rule-scoring method for gene level selection, which grades genes at the same position in the chromosomes. This method is applied to a fuzzy path planner for the shooting of a soccer robot, where each fuzzy rule is encoded as a gene. Simulation and experimental results show the effectiveness and the applicability of the proposed method.