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GA-EDA: Hybrid Evolutionary Algorithm Using Genetic and Estimation of Distribution Algorithms

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Innovations in Applied Artificial Intelligence (IEA/AIE 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3029))

Abstract

Evolutionary techniques are one of the most successful paradigms in the field of optimization. In this paper we present a new approach, named GA-EDA, which is a new hybrid algorithm based on genetic and estimation of distribution algorithms. The original objective is to get benefits from both approaches. In order to perform an evaluation of this new approach a selection of synthetic optimizations problems have been proposed together with two real-world cases. Experimental results show the correctness of our new approach.

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Author information

Authors and Affiliations

  1. Universidad Politécnica de Madrid, Madrid, Spain

    J. M. Peña, V. Robles, P. Larrañaga, V. Herves, F. Rosales & M. S. Pérez

  2. Universidad del País Vasco, San Sebastián, Spain

    J. M. Peña, V. Robles, P. Larrañaga, V. Herves, F. Rosales & M. S. Pérez

Authors
  1. J. M. Peña
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  2. V. Robles
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  3. P. Larrañaga
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  4. V. Herves
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  5. F. Rosales
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  6. M. S. Pérez
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Editor information

Editors and Affiliations

  1. Institute for Information Technology, National Research Council of Canada, 1200 Montreal Read, M-50, K1A 0R6, Ottawa, Ontario, Canada

    Bob Orchard

  2. Institute for Information Technology, National Research Council, Canada

    Chunsheng Yang

  3. Department of Computer Science, Texas State University-San Marcos, Nueces 247, 601 University Drive, TX 78666-4616, San Marcos, USA

    Moonis Ali

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© 2004 Springer-Verlag Berlin Heidelberg

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Peña, J.M., Robles, V., Larrañaga, P., Herves, V., Rosales, F., Pérez, M.S. (2004). GA-EDA: Hybrid Evolutionary Algorithm Using Genetic and Estimation of Distribution Algorithms. In: Orchard, B., Yang, C., Ali, M. (eds) Innovations in Applied Artificial Intelligence. IEA/AIE 2004. Lecture Notes in Computer Science(), vol 3029. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24677-0_38

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  • DOI: https://doi.org/10.1007/978-3-540-24677-0_38

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22007-7

  • Online ISBN: 978-3-540-24677-0

  • eBook Packages: Springer Book Archive

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