Tuning of the structure and parameters of a neural network using an improved genetic algorithm

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

Research output: Journal article publicationJournal articleAcademic researchpeer-review

587 Citations (Scopus)

Abstract

This paper presents the tuning of the structure and parameters of a neural network using an improved genetic algorithm (GA). It will also be shown that the improved GA performs better than the standard GA based on some benchmark test functions. A neural network with switches introduced to its links is proposed. By doing this, the proposed neural network can learn both the input-output relationships of an application and the network structure using the improved GA. The number of hidden nodes is chosen manually by increasing it from a small number until the learning performance in terms of fitness value is good enough. Application examples on sunspot forecasting and associative memory are given to show the merits of the improved GA and the proposed neural network.
Original languageEnglish
Pages (from-to)79-88
Number of pages10
JournalIEEE Transactions on Neural Networks
Volume14
Issue number1
DOIs
Publication statusPublished - 1 Jan 2003

Keywords

  • Genetic algorithm (GA)
  • Neural networks
  • Parameter learning
  • Structure learning

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence

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