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Preference-Driven Co-evolutionary Algorithms Show Promise for Many-Objective Optimisation

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Evolutionary Multi-Criterion Optimization (EMO 2011)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6576))

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Abstract

The simultaneous optimisation of four or more conflicting objectives is now recognised as a challenge for evolutionary algorithms seeking to obtain full representations of trade-off surfaces for the purposes of a posteriori decision-making. Whilst there is evidence that some approaches can outperform both random search and standard Pareto-based methods, best-in-class algorithms have yet to be identified. We consider the concept of co-evolving a population of decision-maker preferences as a basis for determining the fitness of competing candidate solutions. The concept is realised using an existing co-evolutionary approach based on goal vectors. We compare this approach and a variant to three realistic alternatives, within a common optimiser framework. The empirical analysis follows current best practice in the field. As the number of objectives is increased, the preference-driven co-evolutionary approaches tend to outperform the alternatives, according to the hypervolume indicator, and so make a strong claim for further attention in many-objective studies.

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Authors and Affiliations

  1. Department of Automatic Control & Systems Engineering, University of Sheffield, Mappin Street, Sheffield, S1 3JD, UK

    Robin C. Purshouse, Cezar Jalbă & Peter J. Fleming

Authors
  1. Robin C. Purshouse
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  2. Cezar Jalbă
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  3. Peter J. Fleming
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Editor information

Editors and Affiliations

  1. Department of Mathematics, Federal University of Minas Gerais, Av. Antônio Carlos 6627, 31270-901, Belo Horizonte, MG, Brazil

    Ricardo H. C. Takahashi

  2. Department of Mechanical Engineering, Indian Institute of Technology Kanpur, 208 016, Kanpur, India

    Kalyanmoy Deb

  3. Department of Computer Engineering, Centro Federal de Educaçao Tecnológica de Minas Gerais, Av. Amazonas 5253, 30421-169, Belo Horizonte, MG, Brazil

    Elizabeth F. Wanner

  4. Faculty of Economics, University of Catania, Corso Italia 55, 95129, Catania, Italy

    Salvatore Greco

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Purshouse, R.C., Jalbă, C., Fleming, P.J. (2011). Preference-Driven Co-evolutionary Algorithms Show Promise for Many-Objective Optimisation. In: Takahashi, R.H.C., Deb, K., Wanner, E.F., Greco, S. (eds) Evolutionary Multi-Criterion Optimization. EMO 2011. Lecture Notes in Computer Science, vol 6576. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19893-9_10

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  • DOI: https://doi.org/10.1007/978-3-642-19893-9_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-19892-2

  • Online ISBN: 978-3-642-19893-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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