Using genetic algorithms for supervised concept learning

Abstract

The authors consider the application of a genetic algorithm (GA) to a symbolic learning task namely, supervised concept learning from examples. A GA concept learner, GABL, that learns a concept from a set of positive and negative examples is implemented. GABL is run in a batch-incremental mode to facilitate comparison with an incremental concept learner, ID5R. Preliminary results show that, despite minimal system bias, GABL is an effective concept learner and is quite competitive with ID5R as the target concept increases in complexity.<>