DocumentCode
1459260
Title
Exploring the power of genetic search in learning symbolic classifiers
Author
Neri, Filippo ; Saitta, Lorenza
Author_Institution
Dipartimento di Inf., Torino Univ., Italy
Volume
18
Issue
11
fYear
1996
fDate
11/1/1996 12:00:00 AM
Firstpage
1135
Lastpage
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
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;
fLanguage
English
Journal_Title
Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publisher
ieee
ISSN
0162-8828
Type
jour
DOI
10.1109/34.544085
Filename
544085
Link To Document