Abstract
This paper proposes a new approach to cope with multi-objective optimization problems in presence of noise. In the first place, since considering the worst-case performance is important in many real-world optimization problems, a solution is evaluated based on the upper bounds of respective noisy objective functions predicted statistically by multiple sampling. Secondary, a rational way to decide the maximum sample size for the solution is shown. Thirdly, to allocate the computing budget of a proposed evolutionary algorithm only to promising solutions, two pruning techniques are contrived to judge hopeless solutions only by a few sampling and skip the evaluation of the upper bounds for them.
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Tagawa, K., Harada, S. (2014). Multi-Noisy-objective Optimization Based on Prediction of Worst-Case Performance. In: Dediu, AH., Lozano, M., Martín-Vide, C. (eds) Theory and Practice of Natural Computing. TPNC 2014. Lecture Notes in Computer Science, vol 8890. Springer, Cham. https://doi.org/10.1007/978-3-319-13749-0_3
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DOI: https://doi.org/10.1007/978-3-319-13749-0_3
Publisher Name: Springer, Cham
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