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Neural Network training using genetic algorithm with a novel binary encoding
Author(s)
Date Issued
2007
Publisher
Springer Verlag
ISBN
978-354072392-9
ISSN
03029743
Citation
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2007, Vol. 4492 LNCS, Issue PART 2, Pages 371 - 380
Type
Conference Paper
Abstract
Genetic algorithms (GAs) are widely used in the parameter training of Neural Network (NN). In this paper, we investigate GAs based on our proposed novel genetic representation to train the parameters of NN. A splicing/decomposable (S/D) binary encoding is designed based on some theoretical guidance and existing recommendations. Our theoretical and empirical investigations reveal that the S/D binary representation is more proper than other existing binary encodings for GAs' searching. Moreover, a new genotypic distance on the S/D binary space is equivalent to the Euclidean distance on the real-valued space during GAs convergence. Therefore, GAs can reliably and predictably solve problems of bounded complexity and the methods depended on the Euclidean distance for solving different kinds of optimization problems can be directly used on the S/D binary space. This investigation demonstrates that GAs based our proposed binary representation can efficiently and effectively train the parameters of NN. © Springer-Verlag Berlin Heidelberg 2007.
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