The DOI matches its Crossref record; 55 of the 55 references it cites were found in a registry or an index.
Efficient multiobjective optimization employing Gaussian processes, spectral sampling and a genetic algorithm
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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
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Gaussian Processes for Machine Learning
Rasmussen, C. E., & Williams, C. K. I. (2005) · doi:10.7551/mitpress/3206.001.0001 · Cited by 11,705
Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach
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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
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Cited by
Self-Driving Laboratories for Chemistry and Materials Science
Tom, G., Schmid, S. P., Baird, S. G., Cao, Y., Darvish, K., Hao, H., Lo, S., Pablo-García, S., Rajaonson, E. M., Skreta, M., et al. (2024) · Chemical Reviews · doi:10.1021/acs.chemrev.4c00055 · Cited by 707
Recent Advances in Bayesian Optimization
Wang, X., Jin, Y., Schmitt, S., & Olhofer, M. (2023) · ACM Computing Surveys · doi:10.1145/3582078 · Cited by 477
Janet, J. P., Ramesh, S., Duan, C., & Kulik, H. J. (2020) · ACS Central Science · doi:10.1021/acscentsci.0c00026 · Cited by 200
Machine learning and molecular descriptors enable rational solvent selection in asymmetric catalysis
Amar, Y., Schweidtmann, A. M., Deutsch, P., Cao, L., & Lapkin, A. (2019) · Chemical Science · doi:10.1039/c9sc01844a · Cited by 132
Self-citationOptimization of formulations using robotic experiments driven by machine learning DoE
Cao, L., Russo, D., Felton, K., Salley, D., Sharma, A., Keenan, G., Mauer, W., Gao, H., Cronin, L., & Lapkin, A. A. (2021) · Cell Reports Physical Science · doi:10.1016/j.xcrp.2020.100295 · Cited by 75
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Metadata
- Authors
- Bradford, E., Schweidtmann, A. M., & Lapkin, A.
- Venue
- Journal of Global Optimization
- Published
- 2018-02-16
- Volume
- 71
- Issue
- 2
- Pages
- 407-438
- ISSN
- 0925-5001 (ISSN-L)
- 1573-2916
- OpenAlex
- W2788545772
- Cited by
- 266
- References
- 55
- FWCI
- 12.25
- Type
- journal article
- Language
- en
- Open access
- green
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- Bradford, E., Schweidtmann, A. M., & Lapkin, A. (2018). Efficient multiobjective optimization employing Gaussian processes, spectral sampling and a genetic algorithm. Journal of Global Optimization. https://doi.org/10.1007/s10898-018-0609-2
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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.
