USING GENETIC ALGORITHMS FOR CONCEPT-LEARNING

In this article, we explore the use of genetic algorithms (GAs) as a k ey element in the design and implementation of robust concept learning systems. We describe and evaluate a GA-based system called GABIL that continually learns and refines concept classification rules from its interaction with the environment. The use of GAs is motivated by recen t studies showing the effects of various forms of bias built into diff erent concept learning systems, resulting in systems that perform well on certain concept classes (generally, those well matched to the bias es) and poorly on others. By incorporating a GA as the underlying adap tive search mechanism, we are able to construct a concept learning sys tem that has a simple, unified architecture with several important fea tures. First, the system is surprisingly robust even with minimal bias . Second, the system can be easily extended to incorporate traditional forms of bias found in other concept learning systems. Finally, the a rchitecture of the system encourages explicit representation of such b iases and, as a result, provides for an important additional feature: the ability to dynamically adjust system bias. The viability of this a pproach is illustrated by comparing the performance of GABIL with that of four other more traditional concept learners (AQ14, C4.5, ID5R, an d IACL) on a variety of target concepts. We conclude with some observa tions about the merits of this approach and about possible extensions.