Article

Surrogate-Assisted Partial Order-Based Evolutionary Optimisation

Authors:

Günter Rudolph,

Boris NaujoksAuthors Info & Claims

EMO 2017: 9th International Conference on Evolutionary Multi-Criterion Optimization - Volume 10173

Pages 639 - 653

https://doi.org/10.1007/978-3-319-54157-0_43

Published: 19 March 2017 Publication History

Abstract

In this paper, we propose a novel approach SAPEO to support the survival selection process in evolutionary multi-objective algorithms with surrogate models. The approach dynamically chooses individuals to evaluate exactly based on the model uncertainty and the distinctness of the population. We introduce multiple SAPEO variants that differ in terms of the uncertainty they allow for survival selection and evaluate their anytime performance on the BBOB bi-objective benchmark. In this paper, we use a Kriging model in conjunction with an SMS-EMOA for SAPEO. We compare the obtained results with the performance of the regular SMS-EMOA, as well as another surrogate-assisted approach. The results open up general questions about the applicability and required conditions for surrogate-assisted evolutionary multi-objective algorithms to be tackled in the future.

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Cited By

Atkinson LMüller-Bady RKappes MCoello Coello C(2020)Hybrid bayesian evolutionary optimization for hyperparameter tuningProceedings of the 2020 Genetic and Evolutionary Computation Conference Companion10.1145/3377929.3389952(225-226)Online publication date: 8-Jul-2020
https://dl.acm.org/doi/10.1145/3377929.3389952
Volz VNaujoks BAuger AStützle T(2019)On benchmarking surrogate-assisted evolutionary algorithmsProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3319619.3326866(1603-1605)Online publication date: 13-Jul-2019
https://dl.acm.org/doi/10.1145/3319619.3326866
Zhou AZhao HZhang HZhang G(2019)Pareto Optimal Set Approximation by Models: A Linear CaseEvolutionary Multi-Criterion Optimization10.1007/978-3-030-12598-1_36(451-462)Online publication date: 10-Mar-2019
https://dl.acm.org/doi/10.1007/978-3-030-12598-1_36
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Information

Published In

cover image Guide Proceedings

EMO 2017: 9th International Conference on Evolutionary Multi-Criterion Optimization - Volume 10173

March 2017

700 pages

ISBN:9783319541563

Editors:
Heike Trautmann
University of Münster, Münster, Germany
,
Günter Rudolph
TU Dortmund University, Dortmund, Germany
,
Kathrin Klamroth
University of Wuppertal, Wuppertal, Germany
,
Oliver Schütze
CINVESTAV-IPN, Mexico City, Mexico
,
Margaret Wiecek
Clemson University, Clemson, USA
,
Yaochu Jin
University of Surrey, Guildford, United Kingdom
,
Christian Grimme
University of Münster, Münster, Germany

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 19 March 2017

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Cited By

Atkinson LMüller-Bady RKappes MCoello Coello C(2020)Hybrid bayesian evolutionary optimization for hyperparameter tuningProceedings of the 2020 Genetic and Evolutionary Computation Conference Companion10.1145/3377929.3389952(225-226)Online publication date: 8-Jul-2020
https://dl.acm.org/doi/10.1145/3377929.3389952
Volz VNaujoks BAuger AStützle T(2019)On benchmarking surrogate-assisted evolutionary algorithmsProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3319619.3326866(1603-1605)Online publication date: 13-Jul-2019
https://dl.acm.org/doi/10.1145/3319619.3326866
Zhou AZhao HZhang HZhang G(2019)Pareto Optimal Set Approximation by Models: A Linear CaseEvolutionary Multi-Criterion Optimization10.1007/978-3-030-12598-1_36(451-462)Online publication date: 10-Mar-2019
https://dl.acm.org/doi/10.1007/978-3-030-12598-1_36
Volz VRudolph GNaujoks BBosman P(2017)Investigating uncertainty propagation in surrogate-assisted evolutionary algorithmsProceedings of the Genetic and Evolutionary Computation Conference10.1145/3071178.3071249(881-888)Online publication date: 1-Jul-2017
https://dl.acm.org/doi/10.1145/3071178.3071249

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