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Exploring the power of genetic search in learning symbolic classifiers

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2 Author(s)
Neri, F. ; Dipartimento di Inf., Torino Univ., Italy ; Saitta, L.

In this paper we show, in a constructive way, that there are problems for which the use of genetic algorithm based learning systems can be at least as effective as traditional symbolic or connectionist approaches. To this aim, the system REGAL is briefly described, and its application to two classical benchmarks for machine learning is discussed, by comparing the results with the best ones published in the literature

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Pattern Analysis and Machine Intelligence, IEEE Transactions on  (Volume:18 ,  Issue: 11 )