Treed Gaussian Process Regression for Solving Offline Data-Driven Continuous Multiobjective Optimization Problems
Mazumdar, A., López-Ibáñez, M., Chugh, T., Hakanen, J., & Miettinen, K. (2023). Treed Gaussian Process Regression for Solving Offline Data-Driven Continuous Multiobjective Optimization Problems. Evolutionary Computation, 31(4), 375-399. https://doi.org/10.1162/evco_a_00329
Julkaistu sarjassa
Evolutionary ComputationPäivämäärä
2023Oppiaine
Hyvinvoinnin tutkimuksen yhteisöLaskennallinen tiedeMultiobjective Optimization GroupTietotekniikkaPäätöksen teko monitavoitteisestiSchool of WellbeingComputational ScienceMultiobjective Optimization GroupMathematical Information TechnologyDecision analytics utilizing causal models and multiobjective optimizationTekijänoikeudet
© MIT Press 2023
For offline data-driven multiobjective optimization problems (MOPs), no new data is available during the optimization process. Approximation models (or surrogates) are first built using the provided offline data and an optimizer, e.g. a multiobjective evolutionary algorithm, can then be utilized to find Pareto optimal solutions to the problem with surrogates as objective functions. In contrast to online data-driven MOPs, these surrogates cannot be updated with new data and, hence, the approximation accuracy cannot be improved by considering new data during the optimization process. Gaussian process regression (GPR) models are widely used as surrogates because of their ability to provide uncertainty information. However, building GPRs becomes computationally expensive when the size of the dataset is large. Using sparse GPRs reduces the computational cost of building the surrogates. However, sparse GPRs are not tailored to solve offline data-driven MOPs, where good accuracy of the surrogates is needed near Pareto optimal solutions. Treed GPR (TGPR-MO) surrogates for offline data-driven MOPs with continuous decision variables are proposed in this paper. The proposed surrogates first split the decision space into subregions using regression trees and build GPRs sequentially in regions close to Pareto optimal solutions in the decision space to accurately approximate tradeoffs between the objective functions. TGPR-MO surrogates are computationally inexpensive because GPRs are built only in a smaller region of the decision space utilizing a subset of the data. The TGPR-MO surrogates were tested on distance-based visualizable problems with various data sizes, sampling strategies, numbers of objective functions, and decision variables. Experimental results showed that the TGPR-MO surrogates are computationally cheaper and can handle datasets of large size. Furthermore, TGPR-MO surrogates produced solutions closer to Pareto optimal solutions compared to full GPRs and sparse GPRs.
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MIT PressISSN Hae Julkaisufoorumista
1063-6560Asiasanat
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https://converis.jyu.fi/converis/portal/detail/Publication/183034198
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