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Local Search Based on Genetic Algorithms

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Advances in Metaheuristics for Hard Optimization

Part of the book series: Natural Computing Series ((NCS))

Abstract

Genetic Algorithms have been seen as search procedures that can quickly locate high performance regions of vast and complex search spaces, but they are not well suited for fine-tuning solutions, which are very close to optimal ones. However, genetic algorithms may be specifically designed to provide an effective local search as well. In fact, several genetic algorithm models have recently been presented with this aim. In this chapter, we call these algorithms Local Genetic Algorithms.

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Author information

Authors and Affiliations

  1. Dept. of Computing and Numerical Analysis, University of Córdoba, 14071, Córdoba, Spain

    Carlos García-Martínez

  2. Dept. of Computer Science and Artificial Intelligence, University of Granada, 18071, Granada, Spain

    Manuel Lozano

Authors
  1. Carlos García-Martínez
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  2. Manuel Lozano
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Editor information

Editors and Affiliations

  1. Laboratory LiSSi, University of Paris 12, 61 Avenue du Général de Gaulle, 94010, Créteil, France

    Patrick Siarry

  2. School of Computer Science, University of Adelaide, SA 5005, Adelaide, Australia

    Zbigniew Michalewicz

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© 2007 Springer-Verlag Berlin Heidelberg

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García-Martínez, C., Lozano, M. (2007). Local Search Based on Genetic Algorithms. In: Siarry, P., Michalewicz, Z. (eds) Advances in Metaheuristics for Hard Optimization. Natural Computing Series. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72960-0_10

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  • DOI: https://doi.org/10.1007/978-3-540-72960-0_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-72959-4

  • Online ISBN: 978-3-540-72960-0

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