A Surrogate Assisted Approach for Fitness Computation in Robust Optimization over Time
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-62799-6_11
Reference14 articles.
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3. Fox, M., Yang, S., Caraffini, F.: An experimental study of prediction methods in robust optimization over time. In: 2020 IEEE Congress on Evolutionary Computation (CEC), pp. 1–7, July 2020. https://doi.org/10.1109/CEC48606.2020.9185910
4. Fox, M., Yang, S., Caraffini, F.: A new moving peaks benchmark with attractors for dynamic evolutionary algorithms. Swarm Evolut. Comput. 74, 101125 (2022). https://doi.org/10.1016/j.swevo.2022.101125
5. Fu, H., Sendhoff, B., Tang, K., Yao, X.: Robust optimization over time: problem difficulties and benchmark problems. IEEE Trans. Evol. Comput. 19(5), 731–745 (2015). https://doi.org/10.1109/TEVC.2014.2377125
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