An adaptive divide-and-conquer methodology for evolutionary multi-criterion optimisation

Authors:
Robin C. Purshouse;Peter J. Fleming
Affiliations:
Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, UK;Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, UK
Venue:
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Year:
2003

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Abstract

Improved sample-based trade-off surface representations for large numbers of performance criteria can be achieved by dividing the global problem into groups of independent, parallel sub-problems, where possible. This paper describes a progressive criterion-space decomposition methodology for evolutionary optimisers, which uses concepts from parallel evolutionary algorithms and nonparametric statistics. The method is evaluated both quantitatively and qualitatively using a rigorous experimental framework. Proof-of-principle results confirm the potential of the adaptive divide-and-conquer strategy.