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Optimal Design of Neural Nets Using Hybrid Algorithms

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PRICAI 2000 Topics in Artificial Intelligence (PRICAI 2000)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1886))

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Abstract

Selection of the topology of a network and correct parameters for the learning algorithm is a tedious task for designing an optimal Artificial Neural Network (ANN), which is smaller, faster and with a better generalization performance. Genetic algorithm (GA) is an adaptive search technique based on the principles and mechanisms of natural selection and survival of the fittest from natural evolution. Simulated annealing (SA) is a global optimization algorithm that can process cost functions possessing quite arbitrary degrees of nonlinearities, discontinuities and stochasticity but statistically assuring a optimal solution. In this paper we explain how a hybrid algorithm integrating the desirable aspects of GA and SA can be applied for the optimal design of an ANN. This paper is more concerned with the understanding of current theoretical developments of Evolutionary Artificial Neural Networks (EANNs) using GAs and other heuristic procedures and how the proposed hybrid and other heuristic procedures can be combined to produce an optimal ANN.

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Author information

Authors and Affiliations

  1. Gippsland School of Computing & Information Technology, Monash University, Churchill, 3842, Australia

    Ajith Abraham & Baikunth Nath

Authors
  1. Ajith Abraham
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  2. Baikunth Nath
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Editor information

Editors and Affiliations

  1. Institute of Scientific and Industrial Research, Osaka University, 8-1 Mihogaoka, Ibaraki, Osaka, 567-0047, Japan

    Riichiro Mizoguchi

  2. Computer Sciences Laboratory, Research School of Information Sciences and Engineering, Australian National University, Canberra, ACT, 0200, Australia

    John Slaney

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© 2000 Springer-Verlag Berlin Heidelberg

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Abraham, A., Nath, B. (2000). Optimal Design of Neural Nets Using Hybrid Algorithms. In: Mizoguchi, R., Slaney, J. (eds) PRICAI 2000 Topics in Artificial Intelligence. PRICAI 2000. Lecture Notes in Computer Science(), vol 1886. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44533-1_52

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  • DOI: https://doi.org/10.1007/3-540-44533-1_52

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-67925-7

  • Online ISBN: 978-3-540-44533-3

  • eBook Packages: Springer Book Archive

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