Rule Fitness and Pathology in Learning Classifier Systems

Abstract

When applied to reinforcement learning, Learning Classifier Systems (LCS) [5] evolve sets of rules in order to maximise the return they receive from their task environment. They employ a genetic algorithm to generate rules, and to do so must evaluate the fitness of existing rules. In order for the Genetic Algorithm (GA) [4] to produce rules which are better adapted to the task, rule fitness needs somehow to be connected to the rewards received by the system – a credit assignment problem. Precisely how to relate LCS performance to rule fitness has been the subject of much research, and is of great significance because adaptation of rules and LCS alike depends on it.