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The DOI matches its Crossref record; 49 of the 49 references it cites were found in a registry or an index.

Evolutionary multi-objective optimization: a historical view of the field

Coello Coello, C.A.·2006·IEEE Computational Intelligence Magazine

Abstract

This article provides a general overview of the field now known as "evolutionary multi-objective optimization," which refers to the use of evolutionary algorithms to solve problems with two or more (often conflicting) objective functions. Using as a framework the history of this discipline, we discuss some of the most representative algorithms that have been developed so far, as well as some of their applications. Also, we discuss some of the methodological issues related to the use of multi-objective evolutionary algorithms, as well as some of the current and future research trends in the area.

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Metadata

Authors
Coello Coello, C. A.
Venue
IEEE Computational Intelligence Magazine
Published
2006-02
Volume
1
Issue
1
Pages
28-36
ISSN
  • 1556-603X (ISSN-L)
  • 1556-6048
OpenAlex
W2031232041
Cited by
1,446
References
49
FWCI
62.19
Type
journal article
Language
en
Open access
closed

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Coello Coello, C. A. (2006). Evolutionary multi-objective optimization: a historical view of the field. IEEE Computational Intelligence Magazine. https://doi.org/10.1109/mci.2006.1597059

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.