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Multi-Objective Probability Collectives

  • Antony Waldock
  • David Corne
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6024)

Abstract

We describe and evaluate a multi-objective optimisation (MOO) algorithm that works within the Probability Collectives (PC) optimisation framework. PC is an alternative approach to optimization where the optimization process focusses on finding an ideal distribution over the solution space rather than an ideal solution. We describe one way in which MOO can be done in the PC framework, via using a Pareto-based ranking strategy as a single objective. We partially evaluate this via testing on a number of problems, and compare the results with state of the art alternatives. We find that this first multi-objective probability collectives (MOPC) approach performs competitively, indicating both clear promise, and clear room for improvement.

Keywords

Multiobjective Optimization Multiobjective Optimization Problem Multiobjective Genetic Algorithm Probability Collective DEB2 Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Antony Waldock
    • 1
  • David Corne
    • 2
  1. 1.Advanced Technology Centre, BAE SystemsBristolUK
  2. 2.School of MACSHeriot-Watt UniversityEdinburghUK

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