The compact classifier system
Xavier Llorà, Kumara Sastry, David E. Goldberg · 2005
This paper presents an initial analysis of how maximally general and accurate rules can be evolved in a Pittsburgh-style classifier system. In order to be able to perform such analysis we introduce a simple bare-bones Pittsburgh classifier systems---the compact classifier system (CCS)---based on estimation of distribution algorithms. Using a common rule encoding scheme of Pittsburgh classifier systems, CCS maintains a dynamic set of probability vectors that compactly describe a rule set. The compact genetic algorithm is used to evolve each of the initially perturbed probability vectors which represents the rules. Results show how CCS is able to evolve in a compact, simple, and elegant manner rule sets composed by maximally general and accurate rules.