Learning of neural network parameters using a fuzzy genetic algorithm

S. H. Ling, H. K. Lam, Hung Fat Frank Leung, P. K S Tam

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

8 Citations (Scopus)

Abstract

This paper presents the learning of neural network parameters using a fuzzy genetic algorithm (GA). The proposed fuzzy GA is modified from the traditional GA with arithmetic crossover and non-uniform mutation. By introducing modified genetic operations, it will be shown that the performance of the proposed fuzzy GA are better than the traditional GA based on some benchmark test functions. Using the fuzzy GA, the parameters of the neural networks can be tuned. An application example on sunspot forecasting is given to show the merits of the proposed fuzzy GA.
Original languageEnglish
Title of host publicationProceedings of the 2002 Congress on Evolutionary Computation, CEC 2002
PublisherIEEE Computer Society
Pages1928-1933
Number of pages6
Volume2
ISBN (Print)0780372824, 9780780372825
DOIs
Publication statusPublished - 1 Jan 2002
Event2002 Congress on Evolutionary Computation, CEC 2002 - Honolulu, HI, United States
Duration: 12 May 200217 May 2002

Conference

Conference2002 Congress on Evolutionary Computation, CEC 2002
CountryUnited States
CityHonolulu, HI
Period12/05/0217/05/02

ASJC Scopus subject areas

  • Software

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