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Title: Real-coded genetic algorithm with average-bound crossover and wavelet mutation for network parameters learning
Authors: Ling, SH
Leung, FHF 
Keywords: Benchmarking
Genetic algorithms
Genetic engineering
Learning systems
Parameter estimation
Wavelet transforms
Issue Date: 2005
Publisher: IEEE
Source: 2005 IEEE International Joint Conference on Neural Networks (IJCNN) : Montreal, QC, Canada, July 31-August 4, 2005, p. 1325-1330 How to cite?
Abstract: This paper presents the learning of neural network parameters using a real-coded genetic algorithm (RCGA) with proposed crossover and mutation. They are called the average-bound crossover (AveBXover) and wavelet mutation (WM). By introducing the proposed genetic operations, both the solution quality and stability are better than the RCGA with conventional genetic operations. A suite of benchmark test functions are used to evaluate the performance of the proposed algorithm. An application example on an associative memory neural network is used to show the learning performance brought by the proposed RCGA.
ISBN: 0-7803-9048-2
Rights: © 2005 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
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