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. © Springer-Verlag Berlin Heidelberg 2003.
CITATION STYLE
Purshouse, R. C., & Fleming, P. J. (2003). An adaptive divide-and-conquer methodology for evolutionary multi-criterion optimisation. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2632, 133–147. https://doi.org/10.1007/3-540-36970-8_10
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