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On Gradient-Based Local Search to Hybridize Multi-objective Evolutionary Algorithms

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EVOLVE- A Bridge between Probability, Set Oriented Numerics and Evolutionary Computation

Part of the book series: Studies in Computational Intelligence ((SCI,volume 447))

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Abstract

Using evolutionary algorithms when solving multi-objective optimization problems (MOPs) has shown remarkable results during the last decade. As a consolidated research area it counts with a number of guidelines and processes; even though, their efficiency is still a big issue which lets room for improvements. In this chapter we explore the use of gradient-based information to increase efficiency on evolutionary methods, when dealing with smooth real-valued MOPs. We show the main aspects to be considered when building local search operators using the objective function gradients, and when coupling them with evolutionary algorithms. We present an overview of our current methods with discussion about their convenience for particular kinds of problems.

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Author information

Authors and Affiliations

  1. Mathematics Department, ESFM-IPN, Edif. 9 UPALM, 07300, Mexico City, Mexico

    Adriana Lara

  2. Computer Science Department, CINVESTAV-IPN, Av. IPN 2508, Col. San Pedro Zacatenco, 07360, Mexico City, Mexico

    Oliver Schütze & Carlos A. Coello Coello

Authors
  1. Adriana Lara
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  2. Oliver Schütze
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  3. Carlos A. Coello Coello
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Corresponding author

Correspondence to Adriana Lara .

Editor information

Editors and Affiliations

  1. Computer Science and Communications, Research Unit, University of Luxembourg, rue Richard Coudenhove-Kalergi 6, Luxembourg, L-1359, Luxembourg

    Emilia Tantar

  2. , Computer Science and Communications, University of Luxembourg, rue Richard Coudenhove-Kalergi 6, Luxembourg, 1359, Luxembourg

    Alexandru-Adrian Tantar

  3. Computer Science, and Communications, University of Luxembourg, rue Richard Coudenhove-Kalergi 6, Luxembourg, L-1359, Luxembourg

    Pascal Bouvry

  4. Bordeaux Mathematical Institute, INRIA Bordeaux-Sud Ouest, Université Bordeaux I, cours de la Libération 351, Talence Cedex, 33405, France

    Pierre Del Moral

  5. Bâtiment Leyteire, UFR Sciences et Modélisation, Université Bordeaux II, 3ter place de la Victoire, Bordeaux, 33000, France

    Pierrick Legrand

  6. , Computer Science Department, CINVESTAV-IPN, Col. San Pedro Zacatenco 2508, Mexico City, 07360, Mexico

    Carlos A. Coello Coello

  7. , Computer Science Department, CINVESTAV-IPN, Col. San Pedro Zacatenco 2508, Mexico City, 07360, Mexico

    Oliver Schütze

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Lara, A., Schütze, O., Coello Coello, C.A. (2013). On Gradient-Based Local Search to Hybridize Multi-objective Evolutionary Algorithms. In: Tantar, E., et al. EVOLVE- A Bridge between Probability, Set Oriented Numerics and Evolutionary Computation. Studies in Computational Intelligence, vol 447. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32726-1_9

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  • DOI: https://doi.org/10.1007/978-3-642-32726-1_9

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