Evolutionary learning of general fuzzy rules with biased evaluation functions: competition and cooperation

Abstract

Fuzzy rules cooperate in a fuzzy logic controller (FLC) to produce the best action for a given situation. If we have a population of fuzzy rules controlling a device, and we would like to evolve the population to obtain optimal performance by reinforcement learning, rules should compete each other, since we would like to judge their proposals. Therefore, in this approach, cooperation and competition are two opposite, desired activities done by the population members. This may be a problem, if we consider that the evaluation function may be biased, as it may happen, for instance, when we are designing a controlled device such as an autonomous agent. The problem becomes even harder if we would like to learn general rules, i.e., rules containing don't care symbols in their antecedents, thus competing with many groups of other rules, in many different situations. We discuss these issues, and present a solution, implemented in ELF (evolutionary learning of fuzzy rules). We successfully applied ELF to develop autonomous agents, and other fuzzy controlled devices. >