Title :
Exploring the power of genetic search in learning symbolic classifiers
Author :
Neri, Filippo ; Saitta, Lorenza
Author_Institution :
Dipartimento di Inf., Torino Univ., Italy
fDate :
11/1/1996 12:00:00 AM
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
Keywords :
genetic algorithms; learning systems; pattern classification; search problems; symbol manipulation; REGAL; genetic algorithm based learning systems; genetic search; machine learning; symbolic classifiers; Algorithm design and analysis; Design methodology; Expert systems; Genetic algorithms; Humans; Learning systems; Machine learning; Machine learning algorithms; Pattern recognition; Statistics;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
