The DOI matches its Crossref record.
Convergence of evolutionary algorithms in general search spaces
Abstract
This paper provides conditions under which evolutionary algorithms with an elitist selection rule will converge to the global optimum of some function whose domain may be an arbitrary space. These results generalize the previously developed convergence theory for binary and Euclidean search spaces to general search spaces.
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Cited by
Particle swarm optimization: An overview
Poli, R., Kennedy, J., & Blackwell, T. (2007) · Swarm Intelligence · doi:10.1007/s11721-007-0002-0 · Cited by 21,101
Differential Evolution: A Survey of the State-of-the-Art
Das, S., & Suganthan, P. N. (2011) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2010.2059031 · Cited by 5,307
Real-Parameter Optimization with Differential Evolution
Ronkkonen, J., Kukkonen, S., & Price, K. V. (2005) · 2005 IEEE Congress on Evolutionary Computation · doi:10.1109/cec.2005.1554725 · Cited by 566
Metadata
- Authors
- Rudolph, G.
- Venue
- Proceedings of IEEE International Conference on Evolutionary Computation
- Publisher
- IEEE
- Published
- 2002-12-24
- Pages
- 50-54
- OpenAlex
- W2102635079
- Cited by
- 178
- References
- 0
- FWCI
- 5.17
- Type
- conference paper
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Convergence of evolutionary algorithms in general search spaces. https://app.cochat.ai/papers/10.1109/icec.1996.542332
- The paper
- Rudolph, G. (2002). Convergence of evolutionary algorithms in general search spaces. Proceedings of IEEE International Conference on Evolutionary Computation. https://doi.org/10.1109/icec.1996.542332
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.
