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.
|Subtitel||Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion|
|Uitgeverij||Association for Computing Machinery, Inc|
|ISBN van elektronische versie||9781450383516|
|Status||Gepubliceerd - 7 jul. 2021|
|Evenement||2021 Genetic and Evolutionary Computation Conference, GECCO 2021 - Virtual/Online, Lille, Frankrijk|
Duur: 10 jul. 2021 → 14 jul. 2021
|Congres||2021 Genetic and Evolutionary Computation Conference, GECCO 2021|
|Verkorte titel||GECCO 2021|
|Periode||10/07/21 → 14/07/21|
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