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 ...
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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.
作者：
ANDREY, PTARROUX, PUNIV PARIS 07
ECOLE NORMALE SUPERDEPT BIOLBIOCHIM & PHYSIOL DEV LABBIOINFORMAT GRPCNRSF-75230 PARIS 05FRANCE
A new methodological approach to digital image processing applied to the particular case of gray-level image segmentation is introduced. The method is based on a modified and simplified version of classifier systems. ...
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A new methodological approach to digital image processing applied to the particular case of gray-level image segmentation is introduced. The method is based on a modified and simplified version of classifier systems. The labeling function is implemented as a spatially structured set of binary-coded production rules. The labeling is iteratively modified using a distributedgenetic algorithm. Results are presented which illustrate both the mechanisms underlying the functioning of the method and its performance on natural images. The relationships between this approach and other related techniques are discussed and it is shown that it compares favorably with these.
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