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Using genetic algorithms for supervised concept learning

By William M. Spears and Kenneth A. De Long

Abstract

Genetic Algorithms (GAs) have traditionally been used for non-symbolic learning tasks. In this paper we consider me application of a GA to a symbolic learning task, supervised concept learning from examples. A GA concept learner (GABL) is implemented ahat 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, IDSR. Preliminary results suppon ahat. despite minimal system bias, GABL is an ' effective concept learner and is quite competitive with IDSR as me target concept increases in complexity. 1

Year: 1990
OAI identifier: oai:CiteSeerX.psu:10.1.1.161.4073
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