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Exploring the Power of Genetic Search in Learning Symbolic Classifiers
November 1996 (vol. 18 no. 11)
pp. 1135-1141

Abstract—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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Index Terms:
Genetic algorithms, distributed genetic algorithms, classification rules, machine learning, disjunctive concept learning, universal suffrage selection, mushroom dataset, splice junctions dataset, empirical comparison.
Citation:
Filippo Neri, Lorenza Saitta, "Exploring the Power of Genetic Search in Learning Symbolic Classifiers," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, no. 11, pp. 1135-1141, Nov. 1996, doi:10.1109/34.544085
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