The DOI matches its Crossref record; 54 of the 54 references it cites were found in a registry or an index.
Evolutionary dynamic optimization: A survey of the state of the art
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Citations
Cites
No free lunch theorems for optimization
Wolpert, D. H., & Macready, W. G. (1997) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.585893 · Cited by 14,237
Artificial Intelligence through Simulated Evolution
Fogel, L. J., Owens, A. J., & Walsh, M. J. (1966) · International Linear Collider · Cited by 2,796
(2005) · Cited by 2,565
Evolutionary Optimization in Uncertain Environments—A Survey
Jin, Y., & Branke, J. (2005) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2005.846356 · Cited by 1,572
Self-citation(2004) · Lecture Notes in Computer Science · doi:10.1007/b100601 · Cited by 1,147
(2004) · Lecture Notes in Computer Science · doi:10.1007/b98643 · Cited by 957
Evolutionary Optimization in Dynamic Environments
Branke, J. (2002) · Genetic Algorithms and Evolutionary Computation · doi:10.1007/978-1-4615-0911-0 · Cited by 829
Self-citationMemory enhanced evolutionary algorithms for changing optimization problems
Branke, J. (2003) · Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406) · doi:10.1109/cec.1999.785502 · Cited by 743
Self-citationThe Simple Genetic Algorithm: Foundations and Theory
Vose, M. D. (1999) · Medical Entomology and Zoology · Cited by 701
(2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-29353-5 · Cited by 604
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
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
A survey of swarm intelligence for dynamic optimization: Algorithms and applications
Mavrovouniotis, M., Li, C., & Yang, S. (2017) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2016.12.005 · Cited by 585
Self-citationPerformance assessment of the metaheuristic optimization algorithms: an exhaustive review
Halim, A. H., Ismail, I., & Das, S. (2021) · Artificial Intelligence Review · doi:10.1007/s10462-020-09906-6 · Cited by 293
Zhou, Z.-H., Chawla, N. V., Jin, Y., & Williams, G. J. (2014) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2014.2350953 · Cited by 275
Metadata
- Authors
- Nguyen, T. T., Yang, S., & Branke, J.
- Venue
- Swarm and Evolutionary Computation
- Publisher
- Elsevier BV
- Published
- 2012-10
- Volume
- 6
- Pages
- 1-24
- ISSN
- 2210-6502 (ISSN-L)
- OpenAlex
- W2137130066
- Cited by
- 656
- References
- 54
- FWCI
- 41.80
- Type
- journal article
- Language
- en
- Open access
- closed
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- Nguyen, T. T., Yang, S., & Branke, J. (2012). Evolutionary dynamic optimization: A survey of the state of the art. Swarm and Evolutionary Computation. https://doi.org/10.1016/j.swevo.2012.05.001
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.
