Tuning of the Structure and Parameters of a Neural Network Using an Improved Genetic Algorithm

F.H.F. Leung, H.K. Lam, S. Ling, P.K.S. Tam

    Research output: Contribution to journalArticle

    544 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 link s 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
    JournalIEEE Transactions on Neural Networks
    Volume14
    Issue number1
    DOIs
    Publication statusPublished - 2003

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