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Automatic surrogate modelling technique selection based on features of optimization problems

Authors:

Bhupinder Singh Saini,

Manuel Lopez-Ibanez,

Kaisa MiettinenAuthors Info & Claims

GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion

Pages 1765 - 1772

https://doi.org/10.1145/3319619.3326890

Published: 13 July 2019 Publication History

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Abstract

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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Cited By

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Agasiev TKarpenko A(2024)Exploratory Landscape Validation for Bayesian Optimization AlgorithmsMathematics10.3390/math1203042612:3(426)Online publication date: 28-Jan-2024
https://doi.org/10.3390/math12030426
Rios TLanfermann FMenzel S(2024)Large Language Model-assisted Surrogate Modelling for Engineering Optimization2024 IEEE Conference on Artificial Intelligence (CAI)10.1109/CAI59869.2024.00151(796-803)Online publication date: 25-Jun-2024
https://doi.org/10.1109/CAI59869.2024.00151
Kerschke PPreuss MSilva SPaquete L(2023)Exploratory Landscape AnalysisProceedings of the Companion Conference on Genetic and Evolutionary Computation10.1145/3583133.3595058(990-1007)Online publication date: 15-Jul-2023
https://dl.acm.org/doi/10.1145/3583133.3595058
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Index Terms

Automatic surrogate modelling technique selection based on features of optimization problems
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by regression

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Published In

GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion

July 2019

2161 pages

ISBN:9781450367486

DOI:10.1145/3319619

Editor:
Manuel López-Ibáñez
University of Manchester, UK
,
General Chairs:
Anne Auger
Inria and Ecole Polytechnique, France
,
Thomas Stützle
IRIDIA, Université libre de Bruxelles Belgium

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Publication History

Published: 13 July 2019

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SIGEVO

GECCO '19: Genetic and Evolutionary Computation Conference

July 13 - 17, 2019

Prague, Czech Republic

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Agasiev TKarpenko A(2024)Exploratory Landscape Validation for Bayesian Optimization AlgorithmsMathematics10.3390/math1203042612:3(426)Online publication date: 28-Jan-2024
https://doi.org/10.3390/math12030426
Rios TLanfermann FMenzel S(2024)Large Language Model-assisted Surrogate Modelling for Engineering Optimization2024 IEEE Conference on Artificial Intelligence (CAI)10.1109/CAI59869.2024.00151(796-803)Online publication date: 25-Jun-2024
https://doi.org/10.1109/CAI59869.2024.00151
Kerschke PPreuss MSilva SPaquete L(2023)Exploratory Landscape AnalysisProceedings of the Companion Conference on Genetic and Evolutionary Computation10.1145/3583133.3595058(990-1007)Online publication date: 15-Jul-2023
https://dl.acm.org/doi/10.1145/3583133.3595058
Afsar BSilvennoinen JMiettinen K(2023)A Systematic Way of Structuring Real-World Multiobjective Optimization ProblemsEvolutionary Multi-Criterion Optimization10.1007/978-3-031-27250-9_42(593-605)Online publication date: 9-Mar-2023
https://doi.org/10.1007/978-3-031-27250-9_42
Thomaser AKononova AVogt MBäck TWagner M(2022)One-shot optimization for vehicle dynamics control systemsProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3520304.3533979(2036-2045)Online publication date: 9-Jul-2022
https://dl.acm.org/doi/10.1145/3520304.3533979
Sun HWu KWang TWang W(2022)Towards Fair and Robust Classification2022 IEEE 7th European Symposium on Security and Privacy (EuroS&P)10.1109/EuroSP53844.2022.00030(356-376)Online publication date: Jun-2022
https://doi.org/10.1109/EuroSP53844.2022.00030
Renau QDreo JDoerr CDoerr B(2021)Towards Explainable Exploratory Landscape Analysis: Extreme Feature Selection for Classifying BBOB FunctionsApplications of Evolutionary Computation10.1007/978-3-030-72699-7_2(17-33)Online publication date: 1-Apr-2021
https://doi.org/10.1007/978-3-030-72699-7_2
Bossek JDoerr CKerschke PCoello Coello C(2020)Initial design strategies and their effects on sequential model-based optimizationProceedings of the 2020 Genetic and Evolutionary Computation Conference10.1145/3377930.3390155(778-786)Online publication date: 25-Jun-2020
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Afsar BPodkopaev DMiettinen K(2020)Data-driven Interactive Multiobjective Optimization: Challenges and a Generic Multi-agent ArchitectureProcedia Computer Science10.1016/j.procs.2020.08.030176(281-290)Online publication date: 2020
https://doi.org/10.1016/j.procs.2020.08.030
Kerschke PPreuss MAuger AStützle T(2019)Exploratory landscape analysisProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3319619.3323389(1137-1155)Online publication date: 13-Jul-2019
https://dl.acm.org/doi/10.1145/3319619.3323389

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Automatic selection for general surrogate models

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Transfer prior knowledge from surrogate modelling: A meta-learning approach

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