Automatic surrogate modelling technique selection based on features of optimization problems
Saini, B. S., Lopez-Ibanez, M., & Miettinen, K. (2019). Automatic surrogate modelling technique selection based on features of optimization problems. In GECCO '19 : Proceedings of the Genetic and Evolutionary Computation Conference : Companion Volume (pp. 1765-1772). ACM. https://doi.org/10.1145/3319619.3326890
Date
2019Copyright
© 2019 Association for Computing Machinery
A typical scenario when solving industrial single or multiobjective optimization problems is that no explicit formulation of the problem is available. Instead, a dataset containing vectors of decision variables together with their objective function value(s) is given and a surrogate model (or metamodel) is build from the data and used for optimization and decision-making. This data-driven optimization process strongly depends on the ability of the surrogate model to predict the objective value of decision variables not present in the original dataset. Therefore, the choice of surrogate modelling technique is crucial. While many surrogate modelling techniques have been discussed in the literature, there is no standard procedure that will select the best technique for a given problem.
In this work, we propose the automatic selection of a surrogate modelling technique based on exploratory landscape features of the optimization problem that underlies the given dataset. The overall idea is to learn offline from a large pool of benchmark problems, on which we can evaluate a large number of surrogate modelling techniques. When given a new dataset, features are used to select the most appropriate surrogate modelling technique. The preliminary experiments reported here suggest that the proposed automatic selector is able to identify high-accuracy surrogate models as long as an appropriate classifier is used for selection.
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Publisher
ACMParent publication ISBN
978-1-4503-6748-6Conference
Genetic and Evolutionary Computation ConferenceIs part of publication
GECCO '19 : Proceedings of the Genetic and Evolutionary Computation Conference : Companion VolumeKeywords
Publication in research information system
https://converis.jyu.fi/converis/portal/detail/Publication/32153347
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Related funder(s)
Academy of FinlandFunding program(s)
Academy Project, AoF
Additional information about funding
This research was partly supported by the Academy of Finland (grant number 287496, project DESDEO). This related to the thematic research area DEMO (Decision Analytics utilizing Causal Models and Multiobjective Optimization, jyu.fi/demo) of the University of Jyvaskyla.License
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