The DOI matches its Crossref record; 58 of the 58 references it cites were found in a registry or an index.
Parameter tuning for configuring and analyzing evolutionary algorithms
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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
Genetic algorithms in search, optimization, and machine learning
(1989) · Choice Reviews Online · doi:10.5860/choice.27-0936 · Cited by 49,010
Genetic Algorithms + Data Structures = Evolution Programs
Michalewicz, Z. (1992) · Artificial Intelligence · doi:10.1007/978-3-662-02830-8 · Cited by 11,553
Goldberg, D. E. (1989) · Medical Entomology and Zoology · Cited by 10,109
Introduction to Evolutionary Computing
Eiben, A. E., & Smith, J. E. (2003) · Natural Computing Series · doi:10.1007/978-3-662-05094-1 · Cited by 4,176
Self-citationDifferential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization
Qin, A. K., Huang, V. L., & Suganthan, P. N. (2009) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2008.927706 · Cited by 3,614
(2005) · Cited by 2,565
Multiple Comparison Procedures
Hochberg, Y., & Tamhane, A. C. (1987) · Wiley Series in Probability and Statistics · doi:10.1002/9780470316672 · Cited by 2,389
Multiple Comparison Procedures
(2013) · An Introduction to Statistical Concepts · doi:10.4324/9780203137819-17 · Cited by 2,109
Adaptive Particle Swarm Optimization
Zhan, Z.-H., Zhang, J., Li, Y., & Chung, H. S.-H. (2009) · IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) · doi:10.1109/tsmcb.2009.2015956 · Cited by 2,075
Cited by
A comprehensive review of firefly algorithms
Fister, I., Fister, I., Yang, X.-S., & Brest, J. (2013) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2013.06.001 · Cited by 1,308
Exploration and exploitation in evolutionary algorithms: A survey
Črepinšek, M., Liu, S.-H., & Mernik, M. (2013) · ACM Computing Surveys · doi:10.1145/2480741.2480752 · Cited by 1,298
Nature-inspired optimization algorithms: Challenges and open problems
Yang, X.-S. (2020) · Journal of Computational Science · doi:10.1016/j.jocs.2020.101104 · Cited by 429
A survey of swarm and evolutionary computing approaches for deep learning
Darwish, A., Hassanien, A. E., & Das, S. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-019-09719-2 · Cited by 200
A survey on nature-inspired algorithms and its applications in the Internet of Vehicles
Sharma, S., & Kaushik, B. (2021) · International Journal of Communication Systems · doi:10.1002/dac.4895 · Cited by 14
Metadata
- Authors
- Eiben, A. E., & Smit, S. K.
- Venue
- Swarm and Evolutionary Computation
- Publisher
- Elsevier BV
- Published
- 2011-03
- Volume
- 1
- Issue
- 1
- Pages
- 19-31
- ISSN
- 2210-6502 (ISSN-L)
- OpenAlex
- W2132366653
- Cited by
- 622
- References
- 58
- FWCI
- 38.44
- Type
- journal article
- Language
- en
- Open access
- closed
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- Eiben, A. E., & Smit, S. K. (2011). Parameter tuning for configuring and analyzing evolutionary algorithms. Swarm and Evolutionary Computation. https://doi.org/10.1016/j.swevo.2011.02.001
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.
