Learning Concept Classification Rules using Genetic Algorithms,
Kenneth DeJong, William M. Spears · 1990
In this paper we explore the use of an adaptive search technique (genetic algorithms) to construct a system GABIL which continually learns and refines concept classification rules from its interaction with the environment. The performance of the system is measured on a set of concept learning problems and compared with the performance of two existing systems: ID5R and C4.5. Preliminary results support that, despite minimal system bias, GABIL is an effective concept learner and is quite competitive with ID5R and C4.5 as the target concept increases in complexity. 1 Introduction An important requirement for both natural and artificial organisms is the ability to acquire concept classification rules from interactions with their environment. In this paper we explore the use of an adaptive search technique, namely Genetic Algorithms (GAs), as the central mechanism for building a system which continually learns and refines concept classification rules from its interaction with the environm...