Learning Classifier Systems

Abstract

This chapter introduces an evolutionary approach to machine learning tasks working with rule sets, rather than parse trees, to represent knowledge. In learning classifier systems (LCS) the evolutionary algorithm acts as a rule discovery component. LCS systems are used primarily in applications where the objective is to evolve a system that will respond to the current state of its environment (i.e., the inputs to the system) by suggesting a response that in some way maximises (future) reward from the environment.¹ Specifically, the idealised result of running an LCS is the evolution of a rule base that covers the space of possible inputs and suggests the most appropriate actions for each. Through LCS algorithms we also demonstrate evolution where cooperation between the population members (i.e., rules) is crucial. In this aspect LCS systems differ significantly from the other four members of the evolutionary algorithm family, where individuals strictly compete with each other.