Abstract
When optimizing for multiple environments, one usually needs to sacrifice performance in one environment in order to gain better performance in another. Ultimately, there may not be a single solution that meets the performance requirements for all environments. In this paper, we propose to find multiple solutions that each serve a certain group of environments. We call this formulation Robust Optimization with Multiple Solutions (ROMS). Two evolutionary approaches to ROMS are proposed, namely direct evolution and two-phase evolution. A benchmark problem generator is also suggested to produce uniform-random ROMS problems. The two approaches are then experimentally studied on a variety of synthetic problems.
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Yang, P., Tang, K., Li, L., Qin, A.K. (2015). Evolutionary Robust Optimization with Multiple Solutions. In: Handa, H., Ishibuchi, H., Ong, YS., Tan, K. (eds) Proceedings of the 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems, Volume 1. Proceedings in Adaptation, Learning and Optimization, vol 1. Springer, Cham. https://doi.org/10.1007/978-3-319-13359-1_47
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DOI: https://doi.org/10.1007/978-3-319-13359-1_47
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13358-4
Online ISBN: 978-3-319-13359-1
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