Automatic rule generation for fuzzy logic controllers using rule-level co-evolution of subpopulations

In this paper, we propose a rule-level co-evolutionary approach using multiple subpopulations to evolve fuzzy logic controllers (FLCs). Each rule is used as the individual and the subpopulations each comprising a number of candidate rules co-evolve such that the rules belonging to the same subpopulation compete while those in different subpopulations cooperate to achieve the goal of finding a better FLC. During this process, the rules within each subpopulation become specialized into a kind of expert in the corresponding problem domain. For this approach, a simple credit assignment scheme for rule evaluation is introduced to effectively reduce the search space. The superiority of the proposed algorithm over traditional FLC-level evolution approach has been demonstrated by evolving FLCs for a typical nonlinear control problem-the ball and beam system