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A New Crossover Operator and Its Application to Artificial Neural Networks Evolution
Md. Monirul ISLAM Kazuyuki MURASE
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2001/09/01
Print ISSN: 0916-8532
Type of Manuscript: PAPER
artificial neural network, simulated evolution, learning, classification error,
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The design of artificial neural networks (ANNs) through simulated evolution has been investigated for many years. The use of genetic algorithms (GAs) for such evolution suffers a prominent problem known as the permutation problem or the competing convention problem. This paper proposes a new crossover operator, which we call the selected node crossover (SNX), to overcome the permutation problem of GAs for evolving ANNs. A GA-based evolutionary system (GANet) using the SNX for evolving three layered feedforward ANNs architecture with weight learning is described. GANet uses one crossover and one mutation operators sequentially. If the first operator is successful then the second operator is not applied. GANet is less dependent on user-defined control parameters than the conventional evolutionary methods. GANet is applied to a variety of benchmarks including large (26 class) to small (2 class) classification problems. The results show that GANet can produce compact ANN architectures with small classification errors.