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

  • Robin C. Purshouse
  • Cezar Jalbă
  • Peter J. Fleming
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6576)

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.

Keywords

many-objective optimisation co-evolution comparative study 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Robin C. Purshouse
    • 1
  • Cezar Jalbă
    • 1
  • Peter J. Fleming
    • 1
  1. 1.Department of Automatic Control & Systems EngineeringUniversity of SheffieldSheffieldUK

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