Using Genetic Algorithms For Supervised Concept Learning

Genetic Algorithms (GAs) have traditionally been used for non-symbolic learning tasks. In this paper we consider the application of a GA to a symbolic learning task, supervised concept learning from examples. A GA concept learner (GABL) is implemented that learns a concept from a set of positive and negative examples. GABL is run in a batchincremental mode to facilitate comparison with an incremental concept learner, ID5R. Preliminary results support that, despite minimal system bias, GABL is an effective concept learner and is quite competitive with ID5R as the target concept increases in complexity. 1. Introduction There is a common misconception in the machine learning community that Genetic Algorithms (GAs) are primarily useful for non-symbolic learning tasks. This perception comes from the historically heavy use of GAs for complex parameter optimization problems. In the machine learning field there are many interesting parameter tuning problems to which GAs have been and can be ..