Hybrid Dynamic Resampling Algorithms for Evolutionary Multi-objective Optimization of Invariant-Noise Problems

Abstract

In Simulation-based Evolutionary Multi-objective Optimization (EMO) the available time for optimization usually is limited. Since many real-world optimization problems are stochastic models, the optimization algorithm has to employ a noise compensation technique for the objective values. This article analyzes Dynamic Resampling algorithms for handling the… (More)
DOI: 10.1007/978-3-319-31153-1_21

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@inproceedings{Siegmund2016HybridDR, title={Hybrid Dynamic Resampling Algorithms for Evolutionary Multi-objective Optimization of Invariant-Noise Problems}, author={Florian Siegmund and Amos H. C. Ng and Kalyanmoy Deb}, booktitle={EvoApplications}, year={2016} }