Illinois Genetic Algorithms Laboratory

Llorà, X., Sastry, K., Goldberg, D.E. (2005)
TR No.: 2005019 | Download PDF | Download PS

Abstract:
This paper presents an 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 an analysis we introduce a
simple bare-bones Pittsburgh-style classifier systems—the compact classifier system (CCS)—based on estimation of distribution algorithms. Using a common rule encoding schemes of Pittsburgh-style classifier systems,
CCS mantains a dynamic set of probability vectors that compactly describe a rule set. The compact genetic algorithm is used to evolve each of
the initially perturbated probability vectors. Results show how CCS is able to evolve in a compact, simple, and elegant manner rule sets composed by maximally
general and accurate rules. The initial theoretical analysis
and results also show that traditional encoding schemes used by
Pittsburgh-style classifiers add an extra facet of diffiiculty.
Such a bias plays a central role on the overall performance and scalability
of CCS and other Pittsburgh-style systems using such encoding schemes.