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What Makes an Instance Difficult for Black-Box 0–1 Evolutionary Multiobjective Optimizers?

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Artificial Evolution (EA 2013)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8752))

Abstract

This paper investigates the correlation between the characteristics extracted from the problem instance and the performance of a simple evolutionary multiobjective optimization algorithm. First, a number of features are identified and measured on a large set of enumerable multiobjective NK-landscapes with objective correlation. A correlation analysis is conducted between those attributes, including low-level features extracted from the problem input data as well as high-level features extracted from the Pareto set, the Pareto graph and the fitness landscape. Second, we experimentally analyze the (estimated) running time of the global SEMO algorithm to identify a \((1+\varepsilon )\)-approximation of the Pareto set. By putting this performance measure in relation with problem instance features, we are able to explain the difficulties encountered by the algorithm with respect to the main instance characteristics.

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Author information

Authors and Affiliations

  1. LIFL (UMR CNRS 8022), Université Lille 1, Villeneuve d’Ascq, France

    Arnaud Liefooghe

  2. Dolphin, Inria Lille-Nord Europe, Villeneuve d’Ascq, France

    Arnaud Liefooghe

  3. LISIC, Université du Littoral Côte d’Opale, Calais, France

    Sébastien Verel

  4. Faculty of Engineering, Shinshu University, Nagano, Japan

    Hernán Aguirre & Kiyoshi Tanaka

Authors
  1. Arnaud Liefooghe
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  2. Sébastien Verel
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  3. Hernán Aguirre
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  4. Kiyoshi Tanaka
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Corresponding author

Correspondence to Arnaud Liefooghe .

Editor information

Editors and Affiliations

  1. INRIA Bordeaux sud-ouest, Equipe CQFD, Institut de Mathématiques de Bordeaux, Université de Bordeaux, Bordeaux, France

    Pierrick Legrand

  2. Université de Bordeaux, Bordeaux, France

    Marc-Michel Corsini

  3. Université d'Angers, Angers Cedex 01, France

    Jin-Kao Hao

  4. Laboratoire d’Informatique de l’Université de Tours, Ecole Polytechnique de l’Université de Tours, Tours, France

    Nicolas Monmarché

  5. INRA, UMR, Thirverval-Grignon, France

    Evelyne Lutton

  6. Equipe TAO, INRIA Futurs, LRI, University of Paris Sud, Orsay cedex, France

    Marc Schoenauer

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Liefooghe, A., Verel, S., Aguirre, H., Tanaka, K. (2014). What Makes an Instance Difficult for Black-Box 0–1 Evolutionary Multiobjective Optimizers?. In: Legrand, P., Corsini, MM., Hao, JK., Monmarché, N., Lutton, E., Schoenauer, M. (eds) Artificial Evolution. EA 2013. Lecture Notes in Computer Science(), vol 8752. Springer, Cham. https://doi.org/10.1007/978-3-319-11683-9_1

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  • DOI: https://doi.org/10.1007/978-3-319-11683-9_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11682-2

  • Online ISBN: 978-3-319-11683-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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