New Uncertainty Handling Strategies in Multi-objective Evolutionary Optimization Voß, Thomas Trautmann, Heike Igel, Christian Schaefer, Robert Cotta, Carlos Kołodziej, Joanna Rudolph, Günter Since many real-world optimization problems are noisy, vector optimization algorithms that can cope with noise and uncertainty are required. We propose new, robust selection strategies for evolutionary multi-objective optimization in the presence of noise. We apply new measures of uncertainty for estimating the recently introduced Pareto-dominance for uncertain and noisy environments (PDU). The first measure is the inter-quartile range of the outcomes of repeated function evaluations. The second is based on axis-aligned bounding boxes around the upper and lower quantiles of the sampled fitness values in objective space. Experiments on real and artificial problems show promising results. Springer Berlin Heidelberg 2010 info:eu-repo/semantics/conferenceObject doc-type:conferenceObject text http://purl.org/coar/resource_type/c_5794 https://ris.uni-paderborn.de/record/46409 Voß T, Trautmann H, Igel C. New Uncertainty Handling Strategies in Multi-objective Evolutionary Optimization. In: Schaefer R, Cotta C, Kołodziej J, Rudolph G, eds. <i>Parallel Problem Solving from Nature, PPSN XI</i>. Springer Berlin Heidelberg; 2010:260–269. doi:<a href="https://doi.org/10.1007/978-3-642-15871-1_27">https://doi.org/10.1007/978-3-642-15871-1_27</a> eng info:eu-repo/semantics/altIdentifier/doi/https://doi.org/10.1007/978-3-642-15871-1_27 info:eu-repo/semantics/altIdentifier/isbn/978-3-642-15871-1 info:eu-repo/semantics/closedAccess