Abstract
A challenging problem in both engineering and computer science is that of minimising a function for which we have no mathematical formulation available, that is expensive to evaluate, and that contains continuous and integer variables, for example in automatic algorithm configuration. Surrogate-based algorithms are very suitable for this type of problem, but most existing techniques are designed with only continuous or only discrete variables in mind. Mixed-Variable ReLU-based Surrogate Modelling (MVRSM) is a surrogate-based algorithm that uses a linear combination of rectified linear units, defined in such a way that (local) optima satisfy the integer constraints. Unlike other methods, it also has a constant run-time per iteration. This method outperforms the state of the art on several synthetic benchmarks with up to 238 continuous and integer variables, and achieves competitive performance on two real-life benchmarks: XG-Boost hyperparameter tuning and Electrostatic Precipitator optimisation.
Original language | English |
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Title of host publication | GECCO'21 |
Subtitle of host publication | Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion |
Publisher | Association for Computing Machinery, Inc. |
Pages | 1851-1859 |
Number of pages | 9 |
ISBN (Electronic) | 9781450383516 |
DOIs | |
Publication status | Published - 7 Jul 2021 |
Event | 2021 Genetic and Evolutionary Computation Conference, GECCO 2021 - Virtual/Online, Lille, France Duration: 10 Jul 2021 → 14 Jul 2021 https://gecco-2021.sigevo.org/HomePage |
Conference
Conference | 2021 Genetic and Evolutionary Computation Conference, GECCO 2021 |
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Abbreviated title | GECCO 2021 |
Country/Territory | France |
City | Lille |
Period | 10/07/21 → 14/07/21 |
Internet address |
Funding
This work is part of the research programme Real-time data-driven maintenance logistics with project number 628.009.012, which is financed by the Dutch Research Council (NWO). The authors thank Erik Daxberger for providing the code for generating one of MiVaBO’s synthetic test functions, Frederik Rehbach for providing information on the ESP problem, and anonymous reviewers of an earlier version of this paper for providing constructive feedback.
Keywords
- Bayesian optimisation
- expensive optimisation
- mixed-variable optimisation
- surrogate models