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A Memetic Pareto Evolutionary Approach to Artificial Neural Networks

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Book cover AI 2001: Advances in Artificial Intelligence (AI 2001)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2256))

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Abstract

Evolutionary Artificial Neural Networks (EANN) have been a focus of research in the areas of Evolutionary Algorithms (EA) and Artificial Neural Networks (ANN) for the last decade. In this paper, we present an EANN approach based on pareto multi-objective optimization and differential evolution augmented with local search. We call the approach Memetic Pareto Artificial Neural Networks (MPANN). We show empirically that MPANN is capable to overcome the slow training of traditional EANN with equivalent or better generalization.

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Author information

Authors and Affiliations

  1. School of Computer Science, University of New South Wales, ADFA Campus, 2600, Canberra, ACT, Australia

    H.A. Abbass

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  1. H.A. Abbass
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Editor information

Editors and Affiliations

  1. School of Computer and Information Science, University of South Australia, Mawson Lakes, 5095, SA, Australia

    Markus Stumptner  & Dan Corbett  & 

  2. Department of Computer Science, University of Adelaide, 5001, Adelaide, SA, Australia

    Mike Brooks

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© 2001 Springer-Verlag Berlin Heidelberg

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Abbass, H. (2001). A Memetic Pareto Evolutionary Approach to Artificial Neural Networks. In: Stumptner, M., Corbett, D., Brooks, M. (eds) AI 2001: Advances in Artificial Intelligence. AI 2001. Lecture Notes in Computer Science(), vol 2256. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45656-2_1

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  • DOI: https://doi.org/10.1007/3-540-45656-2_1

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42960-9

  • Online ISBN: 978-3-540-45656-8

  • eBook Packages: Springer Book Archive

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