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PESA-II: region-based selection in evolutionary multiobjective optimization

Authors:

David W. Corne,

Nick R. Jerram,

Joshua D. Knowles,

Martin J. OatesAuthors Info & Claims

GECCO'01: Proceedings of the 3rd Annual Conference on Genetic and Evolutionary Computation

Pages 283 - 290

Published: 07 July 2001 Publication History

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Abstract

We describe a new selection technique for evolutionary multiobjective optimization algorithms in which the unit of selection is a hyperbox in objective space. In this technique, instead of assigning a selective fitness to an individual, selective fitness is assigned to the hyperboxes in objective space which are currently occupied by at least one individual in the current approximation to the Pareto frontier. A hyperbox is thereby selected, and the resulting selected individual is randomly chosen from this hyperbox. This method of selection is shown to be more sensitive to ensuring a good spread of development along the Pareto frontier than individual-based selection. The method is implemented in a modern multiobjective evolutionary algorithm, and performance is tested by using Deb's test suite of `T' functions with varying properties. The new selection technique is found to give significantly superior results to the other methods compared, namely PAES, PESA, and SPEA; each is a modern multi-objective optimization algorithm previously found to outperform earlier approaches on various problems.

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cover image Guide Proceedings

GECCO'01: Proceedings of the 3rd Annual Conference on Genetic and Evolutionary Computation

July 2001

1461 pages

ISBN:1558607749

Publisher

Morgan Kaufmann Publishers Inc.

San Francisco, CA, United States

Publication History

Published: 07 July 2001

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Cited By

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https://dl.acm.org/doi/10.1145/3532182
Fan ZLi WCai XLi HWei CZhang QDeb KGoodman E(2020)Difficulty Adjustable and Scalable Constrained Multiobjective Test Problem ToolkitEvolutionary Computation10.1162/evco_a_0025928:3(339-378)Online publication date: 1-Sep-2020
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Guerrero-Peña EKazama Fde Barros Correia PAraújo ACoello Coello C(2020)Solving constrained combinatorial reverse auctions using MOEAsProceedings of the 2020 Genetic and Evolutionary Computation Conference10.1145/3377930.3390205(193-200)Online publication date: 25-Jun-2020
https://dl.acm.org/doi/10.1145/3377930.3390205
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