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New Usage of SOM for Genetic Algorithm  

Kim, Jung-Hwan (서울대학교 컴퓨터공학부)
Moon, Byung-Ro (서울대학교 컴퓨터공학부)
Publication Information
Journal of KIISE:Software and Applications / v.33, no.4, 2006 , pp. 440-448 More about this Journal
Abstract
Self-Organizing Map (SOM) is an unsupervised learning neural network and it is used for preserving the structural relationships in the data without prior knowledge. SOM has been applied in the study of complex problems such as vector quantization, combinatorial optimization, and pattern recognition. This paper proposes a new usage of SOM as a tool for schema transformation hoping to achieve more efficient genetic process. Every offspring is transformed into an isomorphic neural network with more desirable shape for genetic search. This helps genes with strong epistasis to stay close together in the chromosome. Experimental results showed considerable improvement over previous results.
Keywords
Self-Organizing Map; Genetic Algorithm; Recurrent Neural Network; Isomorphic Neural Network; Transformation;
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