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On the Effect of Scalarising Norm Choice in a ParEGO implementation Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Surrogate-Assisted Partial Order-Based Evolutionary Optimisation

Authors : Vanessa Volz, Günter Rudolph, Boris Naujoks

Published in: Evolutionary Multi-Criterion Optimization

Publisher: Springer International Publishing

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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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Footnotes
2
Acknowledgement: The SAPEO concept was developed during the SAMCO Workshop in March 2016 at the Lorentz Center (Leiden, NL). https://​www.​lorentzcenter.​nl This work is part of a project that has received funding from the European Unions Horizon 2020 research and innovation program under grant agreement No 692286.
 
3
Code and visualisations available at: http://​url.​tu-dortmund.​de/​volz.
 
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Metadata
Title
Surrogate-Assisted Partial Order-Based Evolutionary Optimisation
Authors
Vanessa Volz
Günter Rudolph
Boris Naujoks
Copyright Year
2017
Book
Evolutionary Multi-Criterion Optimization

Print ISBN: 978-3-319-54156-3

Electronic ISBN: 978-3-319-54157-0

Copyright Year: 2017

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

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