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Title: Real-coded genetic algorithm with average-bound crossover and wavelet mutation for network parameters learning
Authors: Ling, S. H.
Leung, Frank H. F.
Subjects: 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.
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.
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Type: Conference Paper
ISBN: 0-7803-9048-2
Appears in Collections:EIE Conference Papers & Presentations

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