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
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
A fast and elitist multiobjective genetic algorithm: NSGA-II
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.996017 · Cited by 50,053
Genetic algorithms in search, optimization, and machine learning
(1989) · Choice Reviews Online · doi:10.5860/choice.27-0936 · Cited by 49,010
Multi-Objective Optimization Using Evolutionary Algorithms
Deb, K., & Kalyanmoy, D. (2001) · Andalas University Repository (Andalas University) · Cited by 14,918
Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach
Zitzler, E., & Thiele, L. (1999) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.797969 · Cited by 8,798
Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms
Srinivas, N., & Deb, K. (1994) · Evolutionary Computation · doi:10.1162/evco.1994.2.3.221 · Cited by 6,935
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Zitzler, E., Deb, K., & Thiele, L. (2000) · Evolutionary Computation · doi:10.1162/106365600568202 · Cited by 5,697
A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimization: NSGA-II
Deb, K., Agrawal, S., Pratap, A., & Meyarivan, T. (2000) · Lecture Notes in Computer Science · doi:10.1007/3-540-45356-3_83 · Cited by 5,289
SPEA2: Improving the strength pareto evolutionary algorithm
Zitzler, E., Laumanns, M., & Thiele, L. (2001) · doi:10.3929/ethz-a-004284029 · Cited by 5,008
Handling multiple objectives with particle swarm optimization
Coello, C. A. C., Pulido, G. T., & Lechuga, M. S. (2004) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2004.826067 · Cited by 4,455
Performance assessment of multiobjective optimizers: an analysis and review
Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C. M., & da Fonseca, V. G. (2003) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2003.810758 · Cited by 4,330
Cited by
A review of multiobjective test problems and a scalable test problem toolkit
Huband, S., Hingston, P., Barone, L., & While, L. (2006) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2005.861417 · Cited by 2,090
Bio-inspired computation: Where we stand and what's next
Del Ser, J., Osaba, E., Molina, D., Yang, X.-S., Salcedo-Sanz, S., Camacho, D., Das, S., Suganthan, P. N., Coello Coello, C. A., & Herrera, F. (2019) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2019.04.008 · Cited by 593
Self-citationConstraint Handling in Multiobjective Evolutionary Optimization
Woldesenbet, Y. G., Yen, G. G., & Tessema, B. G. (2009) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2008.2009032 · Cited by 420
Consistencies and Contradictions of Performance Metrics in Multiobjective Optimization
Jiang, S., Ong, Y.-S., Zhang, J., & Feng, L. (2014) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2014.2307319 · Cited by 314
A survey on artificial intelligence trends in spacecraft guidance dynamics and control
Izzo, D., Märtens, M., & Pan, B. (2019) · Astrodynamics · doi:10.1007/s42064-018-0053-6 · Cited by 281
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Evolutionary multi-objective optimization: a historical view of the field. https://app.cochat.ai/papers/10.1109/mci.2006.1597059
- The paper
- 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
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.