The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Multi-objective optimization by genetic algorithms: a review
Abstract
The paper reviews several genetic algorithm (GA) approaches to multi objective optimization problems (MOPs). The keynote point of GAs to MOPs is designing efficient selection/reproduction operators so that a variety of Pareto optimal solutions are generated. From this viewpoint, the paper reviews several devices proposed for multi objective optimization by GAs such as the parallel selection method, the Pareto based ranking, and the fitness sharing. Characteristics of these approaches have been confirmed through computational experiments with a simple example. Moreover, two practical applications of the GA approaches to MOPs are introduced briefly.
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Citations
Cites
Artificial evolution for computer graphics
Sims, K. (1991) · Proceedings of the 18th annual conference on Computer graphics and interactive techniques · doi:10.1145/122718.122752 · Cited by 741
Cited by
An updated survey of GA-based multiobjective optimization techniques
Coello, C. A. (2000) · ACM Computing Surveys · doi:10.1145/358923.358929 · Cited by 773
Cyber-Physical Inconsistency Vulnerability Identification for Safety Checks in Robotic Vehicles
Choi, H., Kate, S., Aafer, Y., Zhang, X., & Xu, D. (2020) · Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3372297.3417249 · Cited by 27
Metadata
- Authors
- Tamaki, H., Kita, H., & Kobayashi, S.
- Venue
- Proceedings of IEEE International Conference on Evolutionary Computation
- Publisher
- IEEE
- Published
- 2002-12-23
- Pages
- 517-522
- OpenAlex
- W2161142631
- Cited by
- 292
- References
- 1
- FWCI
- 18.48
- Type
- conference paper
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Multi-objective optimization by genetic algorithms: a review. https://app.cochat.ai/papers/10.1109/icec.1996.542653
- The paper
- Tamaki, H., Kita, H., & Kobayashi, S. (2002). Multi-objective optimization by genetic algorithms: a review. Proceedings of IEEE International Conference on Evolutionary Computation. https://doi.org/10.1109/icec.1996.542653
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