The DOI matches its Crossref record; 89 of the 89 references it cites were found in a registry or an index.
A Survey of Automatic Parameter Tuning Methods for Metaheuristics
Open accessAbstract
Parameter tuning, that is, to find appropriate parameter settings (or configurations) of algorithms so that their performance is optimized, is an important task in the development and application of metaheuristics. Automating this task, i.e., developing algorithmic procedure to address parameter tuning task, is highly desired and has attracted significant attention from the researchers and practitioners. During last two decades, many automatic parameter tuning approaches have been proposed. This paper presents a comprehensive survey of automatic parameter tuning methods for metaheuristics. A new classification (or taxonomy) of automatic parameter tuning methods is introduced according to the structure of tuning methods. The existing automatic parameter tuning approaches are consequently classified into three categories: 1) simple generate-evaluate methods; 2) iterative generate-evaluate methods; and 3) high-level generate-evaluate methods. Then, these three categories of tuning methods are reviewed in sequence. In addition to the description of each tuning method, its main strengths and weaknesses are discussed, which is helpful for new researchers or practitioners to select appropriate tuning methods to use. Furthermore, some challenges and directions of this field are pointed out for further research.
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Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Optimization by Simulated Annealing
Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983) · Science · doi:10.1126/science.220.4598.671 · Cited by 45,134
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
The Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162
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
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
Efficient Global Optimization of Expensive Black-Box Functions
Jones, D. R., Schonlau, M., & Welch, W. J. (1998) · Journal of Global Optimization · doi:10.1023/a:1008306431147 · Cited by 8,070
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
(2007) · Handbook of Approximation Algorithms and Metaheuristics · doi:10.1201/9781420010749-33 · Cited by 6,607
Information theory, inference, and learning algorithms
(2004) · Choice Reviews Online · doi:10.5860/choice.41-5949 · Cited by 6,362
Cited by
Karimi-Mamaghan, M., Mohammadi, M., Meyer, P., Karimi-Mamaghan, A. M., & Talbi, E.-G. (2022) · European Journal of Operational Research · doi:10.1016/j.ejor.2021.04.032 · Cited by 462
Osaba, E., Villar-Rodriguez, E., Del Ser, J., Nebro, A. J., Molina, D., LaTorre, A., Suganthan, P. N., Coello Coello, C. A., & Herrera, F. (2021) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2021.100888 · Cited by 406
Learning Performance-Improving Code Edits
Shypula, A., Madaan, A., Zeng, Y., Alon, U., Gardner, J., Hashemi, M., Neubig, G., Ranganathan, P., Bastani, O., & Yazdanbakhsh, A. (2023) · arXiv · doi:10.48550/arxiv.2302.07867 · Cited by 146
“, 1994; Talaashrafi, 2022), parameter search (Hamadi & H madi, 2013; Huang et al., 2019; Kaufman et al., 2021), auto-vectorization (Nuzman et al.”
BackgroundInfluential
Metadata
- Authors
- Huang, C., Li, Y., & Yao, X.
- Venue
- IEEE Transactions on Evolutionary Computation
- Published
- 2020-04
- Volume
- 24
- Issue
- 2
- Pages
- 201-216
- ISSN
- 1089-778X (ISSN-L)
- 1941-0026
- OpenAlex
- W2961614712
- Cited by
- 371
- References
- 89
- FWCI
- 16.58
- Type
- journal article
- Language
- en
- Open access
- hybrid
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