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Integration of supervised ART-based neural networks with a hybrid genetic algorithm

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Abstract

In this paper, two evolutionary artificial neural network (EANN) models that are based on integration of two supervised adaptive resonance theory (ART)-based artificial neural networks with a hybrid genetic algorithm (HGA) are proposed. The search process of the proposed EANN models is guided by a knowledge base established by ART with respect to the training data samples. The EANN models explore the search space for “coarse” solutions, and such solutions are then refined using the local search process of the HGA. The performances of the proposed EANN models are evaluated and compared with those from other classifiers using more than ten benchmark data sets. The applicability of the EANN models to a real medical classification task is also demonstrated. The results from the experimental studies demonstrate the effectiveness and usefulness of the proposed EANN models in undertaking pattern classification problems.

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Authors and Affiliations

  1. Faculty of Information Science and Technology, Multimedia University, Melaka Campus, Jalan Ayer Keroh Lama, Bukit Beruang, 75450, Melaka, Malaysia

    Shing Chiang Tan

  2. School of Electrical and Electronic Engineering, University of Science Malaysia, Engineering Campus, Nibong Tebal, 14300, Penang, Malaysia

    Chee Peng Lim

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  1. Shing Chiang Tan
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  2. Chee Peng Lim
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Correspondence to Shing Chiang Tan.

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Tan, S.C., Lim, C.P. Integration of supervised ART-based neural networks with a hybrid genetic algorithm. Soft Comput 15, 205–219 (2011). https://doi.org/10.1007/s00500-010-0679-7

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  • DOI: https://doi.org/10.1007/s00500-010-0679-7

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