Real-coded genetic algorithm with average-bound crossover and wavelet mutation for network parameters learning

S. H. Ling; Hung Fat Frank Leung

doi:10.1109/IJCNN.2005.1556046

Real-coded genetic algorithm with average-bound crossover and wavelet mutation for network parameters learning

S. H. Ling, Hung Fat Frank Leung

The Hong Kong Polytechnic University

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

4 Citations (Scopus)

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.

Original language	English
Title of host publication	Proceedings of the International Joint Conference on Neural Networks, IJCNN 2005
Pages	1325-1330
Number of pages	6
Volume	2
DOIs	https://doi.org/10.1109/IJCNN.2005.1556046
Publication status	Published - 1 Dec 2005
Event	International Joint Conference on Neural Networks, IJCNN 2005 - Montreal, QC, Canada Duration: 31 Jul 2005 → 4 Aug 2005

Conference

Conference	International Joint Conference on Neural Networks, IJCNN 2005
Country/Territory	Canada
City	Montreal, QC
Period	31/07/05 → 4/08/05

ASJC Scopus subject areas

Software
Artificial Intelligence

More information

10.1109/IJCNN.2005.1556046

http://hdl.handle.net/10397/1376

Cite this

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title = "Real-coded genetic algorithm with average-bound crossover and wavelet mutation for network parameters learning",

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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Ling, SH & Leung, HFF 2005, Real-coded genetic algorithm with average-bound crossover and wavelet mutation for network parameters learning. in Proceedings of the International Joint Conference on Neural Networks, IJCNN 2005. vol. 2, 1556046, pp. 1325-1330, International Joint Conference on Neural Networks, IJCNN 2005, Montreal, QC, Canada, 31/07/05. https://doi.org/10.1109/IJCNN.2005.1556046

Real-coded genetic algorithm with average-bound crossover and wavelet mutation for network parameters learning. / Ling, S. H.; Leung, Hung Fat Frank.
Proceedings of the International Joint Conference on Neural Networks, IJCNN 2005. Vol. 2 2005. p. 1325-1330 1556046.

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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Real-coded genetic algorithm with average-bound crossover and wavelet mutation for network parameters learning

Abstract

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ASJC Scopus subject areas

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