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Cited by 451

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On the Evolutionary Optimization of Many Conflicting Objectives

Purshouse, Robin C., Fleming, Peter J.·2007·IEEE Transactions on Evolutionary Computation

Abstract

This study explores the utility of multiobjective evolutionary algorithms (using standard Pareto ranking and diversity-promoting selection mechanisms) for solving optimization tasks with many conflicting objectives. Optimizer behavior is assessed for a grid of mutation and recombination operator configurations. Performance maps are obtained for the dual aims of proximity to, and distribution across, the optimal tradeoff surface. Performance sweet-spots for both variation operators are observed to contract as the number of objectives is increased. Classical settings for recombination are shown to be suitable for small numbers of objectives but correspond to very poor performance for higher numbers of objectives, even when large population sizes are used. Explanations for this behavior are offered via the concepts of dominance resistance and active diversity promotion.

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Metadata

Authors
Purshouse, R. C., & Fleming, P. J.
Venue
IEEE Transactions on Evolutionary Computation
Published
2007-12
Volume
11
Issue
6
Pages
770-784
ISSN
1089-778X (ISSN-L)
OpenAlex
W2133787071
Cited by
451
References
1
FWCI
14.08
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: On the Evolutionary Optimization of Many Conflicting Objectives. https://app.cochat.ai/papers/10.1109/tevc.2007.910138
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Purshouse, R. C., & Fleming, P. J. (2007). On the Evolutionary Optimization of Many Conflicting Objectives. IEEE Transactions on Evolutionary Computation. https://doi.org/10.1109/tevc.2007.910138

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