An improved genetic-algorithm-based neural-tuned neural network

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

Research output: Journal article publicationJournal articleAcademic researchpeer-review

2 Citations (Scopus)


This paper presents a neural-tuned neural network (NTNN), which is trained by an improved genetic algorithm (GA). The NTNN consists of a common neural network and a modified neural network (MNN). In the MNN, a neuron model with two activation functions is introduced. An improved GA is proposed to train the parameters of the proposed network. A set of improved genetic operations are presented, which show superior performance over the traditional GA. The proposed network structure can increase the search space of the network and offer better performance than the traditional feed-forward neural network. Two application examples are given to illustrate the merits of the proposed network and the improved GA.
Original languageEnglish
Pages (from-to)469-492
Number of pages24
JournalInternational Journal of Computational Intelligence and Applications
Issue number4
Publication statusPublished - 1 Dec 2008


  • Genetic algorithm
  • Neural network
  • Pattern recognition
  • Sunspot forecasting

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Theoretical Computer Science


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