The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
Sequential Parameter Optimization
Abstract
Sequential parameter optimization is a heuristic that combines classical and modern statistical techniques to improve the performance of search algorithms. To demonstrate its flexibility, three scenarios are discussed: (1) no experience how to choose the parameter setting of an algorithm is available, (2) a comparison with other algorithms is needed, and (3) an optimization algorithm has to be applied effectively and efficiently to a complex real-world optimization problem. Although sequential parameter optimization relies on enhanced statistical techniques such as design and analysis of computer experiments, it can be performed algorithmically and requires basically the specification of the relevant algorithm's parameters.
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Cites
Classification and Regression Trees.
Gordon, A. D., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984) · Biometrics · doi:10.2307/2530946 · Cited by 23,522
(2005) · Technometrics · doi:10.1198/tech.2005.s303 · Cited by 17,923
A new optimizer using particle swarm theory
Eberhart, R., & Kennedy, J. (2002) · MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science · doi:10.1109/mhs.1995.494215 · Cited by 16,067
R: A Language for Data Analysis and Graphics
Ihaka, R., & Gentleman, R. (1996) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.1996.10474713 · Cited by 11,220
McKay, M. D., Beckman, R. J., & Conover, W. J. (2000) · Technometrics · doi:10.2307/1271432 · Cited by 7,415
Design and Analysis of Computer Experiments
Sacks, J., Welch, W. J., Mitchell, T. J., & Wynn, H. P. (1989) · Statistical Science · doi:10.1214/ss/1177012413 · Cited by 6,919
Fisher, R. A. (1936) · BMJ · doi:10.1136/bmj.1.3923.554-a · Cited by 4,980
Gunst, R. F. (1999) · Technometrics · doi:10.1080/00401706.1999.10485680 · Cited by 4,002
Empirical study of particle swarm optimization
Shi, Y., & Eberhart, R. C. (2003) · Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406) · doi:10.1109/cec.1999.785511 · Cited by 3,945
McKay, M. D., Beckman, R. J., & Conover, W. J. (1979) · Technometrics · doi:10.2307/1268522 · Cited by 3,757
Cited by
Sequential Model-Based Optimization for General Algorithm Configuration
Hutter, F., Hoos, H. H., & Leyton-Brown, K. (2011) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-25566-3_40 · Cited by 2,469
The irace package: Iterated racing for automatic algorithm configuration
López-Ibáñez, M., Dubois-Lacoste, J., Pérez Cáceres, L., Birattari, M., & Stützle, T. (2016) · Operations Research Perspectives · doi:10.1016/j.orp.2016.09.002 · Cited by 1,761
Benchmarking in Optimization: Best Practice and Open Issues
Bartz-Beielstein, T., Doerr, C., Berg, D. V. D., Bossek, J., Chandrasekaran, S., Eftimov, T., Fischbach, A., Kerschke, P., La Cava, W., Lopez-Ibanez, M., et al. (2020) · arXiv · doi:10.48550/arxiv.2007.03488 · Cited by 214
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Metadata
- Authors
- Bartz-Beielstein, T., Lasarczyk, C. W. G., & Preuss, M.
- Venue
- 2005 IEEE Congress on Evolutionary Computation
- Publisher
- IEEE
- Published
- 2005-12-13
- Volume
- 1
- Pages
- 773-780
- OpenAlex
- W2117643431
- Cited by
- 300
- References
- 30
- FWCI
- 3.34
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Bartz-Beielstein, T., Lasarczyk, C. W. G., & Preuss, M. (2005). Sequential Parameter Optimization. 2005 IEEE Congress on Evolutionary Computation. https://doi.org/10.1109/cec.2005.1554761
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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.
