The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Single- and multiobjective evolutionary optimization assisted by Gaussian random field metamodels
Open accessAbstract
This paper presents and analyzes in detail an efficient search method based on evolutionary algorithms (EA) assisted by local Gaussian random field metamodels (GRFM). It is created for the use in optimization problems with one (or many) computationally expensive evaluation function(s). The role of GRFM is to predict objective function values for new candidate solutions by exploiting information recorded during previous evaluations. Moreover, GRFM are able to provide estimates of the confidence of their predictions. Predictions and their confidence intervals predicted by GRFM are used by the metamodel assisted EA. It selects the promising members in each generation and carries out exact, costly evaluations only for them. The extensive use of the uncertainty information of predictions for screening the candidate solutions makes it possible to significantly reduce the computational cost of singleand multiobjective EA. This is adequately demonstrated in this paper by means of mathematical test cases and a multipoint airfoil design in aerodynamics
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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
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
Completely Derandomized Self-Adaptation in Evolution Strategies
Hansen, N., & Ostermeier, A. (2001) · Evolutionary Computation · doi:10.1162/106365601750190398 · Cited by 4,397
Gaussian elimination is not optimal
Strassen, V. (1969) · Numerische Mathematik · doi:10.1007/bf02165411 · Cited by 2,509
Cited by
Expensive Multiobjective Optimization by MOEA/D With Gaussian Process Model
Zhang, Q., Liu, W., Tsang, E., & Virginas, B. (2010) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2009.2033671 · Cited by 746
A Survey on the Hypervolume Indicator in Evolutionary Multiobjective Optimization
Shang, K., Ishibuchi, H., He, L., & Pang, L. M. (2021) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2020.3013290 · Cited by 380
Multi-objective Bayesian optimization using Pareto-frontier entropy
Suzuki, S., Takeno, S., Tamura, T., Shitara, K., & Karasuyama, M. (2019) · arXiv · doi:10.48550/arxiv.1906.00127 · Cited by 40
Metadata
- Authors
- Emmerich, M. T. M., Giannakoglou, K. C., & Naujoks, B.
- Venue
- IEEE Transactions on Evolutionary Computation
- Published
- 2006-08
- Volume
- 10
- Issue
- 4
- Pages
- 421-439
- ISSN
- 1089-778X (ISSN-L)
- OpenAlex
- W2136918060
- Cited by
- 759
- References
- 4
- FWCI
- 19.48
- Type
- journal article
- Language
- en
- Open access
- green
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- Emmerich, M. T. M., Giannakoglou, K. C., & Naujoks, B. (2006). Single- and multiobjective evolutionary optimization assisted by Gaussian random field metamodels. IEEE Transactions on Evolutionary Computation. https://doi.org/10.1109/tevc.2005.859463
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