The DOI matches its Crossref record; 76 of the 76 references it cites were found in a registry or an index.
A survey on multi-objective evolutionary algorithms for many-objective problems
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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
Rayward-Smith, V. J. (1991) · Journal of the Operational Research Society · doi:10.1057/jors.1991.155 · Cited by 16,821
Multi-Objective Optimization Using Evolutionary Algorithms
Deb, K., & Kalyanmoy, D. (2001) · Andalas University Repository (Andalas University) · Cited by 14,918
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
Zhang, Q., & Li, H. (2007) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2007.892759 · Cited by 10,069
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
Global Sensitivity Analysis. The Primer
Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., & Tarantola, S. (2007) · doi:10.1002/9780470725184 · Cited by 6,430
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Zitzler, E., Deb, K., & Thiele, L. (2000) · Evolutionary Computation · doi:10.1162/106365600568202 · Cited by 5,697
Evolutionary Algorithms for Solving Multi-Objective Problems
(2007) · Genetic and Evolutionary Computation Series · doi:10.1007/978-0-387-36797-2 · Cited by 5,686
SPEA2: Improving the strength pareto evolutionary algorithm
Zitzler, E., Laumanns, M., & Thiele, L. (2001) · doi:10.3929/ethz-a-004284029 · Cited by 5,008
Nonlinear Multiobjective Optimization
Miettinen, K. (1998) · International Series in Operations Research & Management Science · doi:10.1007/978-1-4615-5563-6 · Cited by 4,710
Cited by
Grasshopper optimization algorithm for multi-objective optimization problems
Mirjalili, S. Z., Mirjalili, S., Saremi, S., Faris, H., & Aljarah, I. (2018) · Applied Intelligence · doi:10.1007/s10489-017-1019-8 · Cited by 758
Performance of Decomposition-Based Many-Objective Algorithms Strongly Depends on Pareto Front Shapes
Ishibuchi, H., Setoguchi, Y., Masuda, H., & Nojima, Y. (2017) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2016.2587749 · Cited by 592
A practical guide to multi-objective reinforcement learning and planning
Hayes, C. F., Rădulescu, R., Bargiacchi, E., Källström, J., Macfarlane, M., Reymond, M., Verstraeten, T., Zintgraf, L. M., Dazeley, R., Heintz, F., et al. (2022) · Autonomous Agents and Multi-Agent Systems · doi:10.1007/s10458-022-09552-y · Cited by 400
Metadata
- Authors
- von Lücken, C., Barán, B., & Brizuela, C.
- Venue
- Computational Optimization and Applications
- Published
- 2014-02-20
- ISSN
- 0926-6003 (ISSN-L)
- 1573-2894
- OpenAlex
- W1977398789
- Cited by
- 297
- References
- 76
- FWCI
- 39.52
- Type
- journal article
- Language
- en
- Open access
- closed
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- von Lücken, C., Barán, B., & Brizuela, C. (2014). A survey on multi-objective evolutionary algorithms for many-objective problems. Computational Optimization and Applications. https://doi.org/10.1007/s10589-014-9644-1
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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.
