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

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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 languageEnglish
Title of host publicationProceedings of the International Joint Conference on Neural Networks, IJCNN 2005
Pages1325-1330
Number of pages6
Volume2
DOIs
Publication statusPublished - 1 Dec 2005
EventInternational Joint Conference on Neural Networks, IJCNN 2005 - Montreal, QC, Canada
Duration: 31 Jul 20054 Aug 2005

Conference

ConferenceInternational Joint Conference on Neural Networks, IJCNN 2005
Country/TerritoryCanada
CityMontreal, QC
Period31/07/054/08/05

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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