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The DOI matches its Crossref record; 46 of the 46 references it cites were found in a registry or an index.

ParEGO: a hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems

Knowles, J.·2006·IEEE Transactions on Evolutionary Computation
DOI 10.1109/tevc.2005.851274
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Abstract

This paper concerns multiobjective optimization in scenarios where each solution evaluation is financially and/or temporally expensive. We make use of nine relatively low-dimensional, nonpathological, real-valued functions, such as arise in many applications, and assess the performance of two algorithms after just 100 and 250 (or 260) function evaluations. The results show that NSGA-II, a popular multiobjective evolutionary algorithm, performs well compared with random search, even within the restricted number of evaluations used. A significantly better performance (particularly, in the worst case) is, however, achieved on our test set by an algorithm proposed herein-ParEGO-which is an extension of the single-objective efficient global optimization (EGO) algorithm of Jones et al. ParEGO uses a design-of-experiments inspired initialization procedure and learns a Gaussian processes model of the search landscape, which is updated after every function evaluation. Overall, ParEGO exhibits a promising performance for multiobjective optimization problems where evaluations are expensive or otherwise restricted in number.

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Metadata

Authors
Knowles, J.
Venue
IEEE Transactions on Evolutionary Computation
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2006-02
Volume
10
Issue
1
Pages
50-66
ISSN
1089-778X (ISSN-L)
DOI
10.1109/tevc.2005.851274
OpenAlex
W2111526171
Cited by
1,254
References
46
FWCI
33.49
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: ParEGO: a hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems. https://app.cochat.ai/papers/10.1109/tevc.2005.851274
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Knowles, J. (2006). ParEGO: a hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems. IEEE Transactions on Evolutionary Computation. https://doi.org/10.1109/tevc.2005.851274

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