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Richter/etal/2020a: Model-Based Optimization with Concept Drifts

Bibtype Inproceedings
Bibkey Richter/etal/2020a
Author Richter, Jakob and Shi, Junjie and Chen, Jian-Jia and Rahnenführer, Jörg and Lang, Michel
Title Model-Based Optimization with Concept Drifts
Booktitle Proceedings of the 2020 Genetic and Evolutionary Computation Conference
Series GECCO '20
Pages 877?885
Address New York, NY, USA
Publisher Association for Computing Machinery
Abstract Model-based Optimization (MBO) is a method to optimize expensive black-box functions that uses a surrogate to guide the search. We propose two practical approaches that allow MBO to optimize black-box functions where the relation between input and output changes over time, which are known as dynamic optimization problems (DOPs). The window approach trains the surrogate only on the most recent observations, and the time-as-covariate approach includes the time as an additional input variable in the surrogate, giving it the ability to learn the effect of the time on the outcomes. We focus on problems where the change happens systematically and label this systematic change concept drift. To benchmark our methods we define a set of benchmark functions built from established synthetic static functions that are extended with controlled drifts. We evaluate how the proposed approaches handle scenarios of no drift, sudden drift and incremental drift. The results show that both new methods improve the performance if a drift is present. For higher-dimensional multimodal problems the window approach works best and on lower-dimensional problems, where it is easier for the surrogate to capture the influence of the time, the time-as-covariate approach works better.
Year 2020
Projekt SFB876-A3
Url https://doi.org/10.1145/3377930.3390175
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