Surrogate-assisted Multi-objective Optimization via Genetic Programming Based Symbolic Regression

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Abstract

Surrogate-assisted optimization algorithms are a commonly used technique to solve expensive-evaluation problems, in which a regression model is built to replace an expensive function. In some acquisition functions, the only requirement for a regression model is the predictions. However, some other acquisition functions also require a regression model to estimate the “uncertainty” of the prediction, instead of merely providing predictions. Unfortunately, very few statistical modeling techniques can achieve this, such as Kriging/Gaussian processes, and recently proposed genetic programming-based (GP-based) symbolic regression with Kriging (GP2). Another method is to use a bootstrapping technique in GP-based symbolic regression to estimate prediction and its corresponding uncertainty. This paper proposes to use GP-based symbolic regression and its variants to solve multi-objective optimization problems (MOPs), which are under the framework of a surrogate-assisted multi-objective optimization algorithm (SMOA). Kriging and random forest are also compared with GP-based symbolic regression and GP2. Experiment results demonstrate that the surrogate models using the GP2 strategy can improve SMOA’s performance.

OriginalspracheEnglisch
TitelEvolutionary Multi-Criterion Optimization - 12th International Conference, EMO 2023, Proceedings
Redakteure/-innenMichael Emmerich, André Deutz, Hao Wang, Anna V. Kononova, Boris Naujoks, Ke Li, Kaisa Miettinen, Iryna Yevseyeva
Herausgeber (Verlag)Springer
Seiten176-190
Seitenumfang15
ISBN (Print)9783031272493
DOIs
PublikationsstatusVeröffentlicht - 2023
Veranstaltung12th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2023 - Leiden, Niederlande
Dauer: 20 März 202324 März 2023

Publikationsreihe

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Band13970 LNCS
ISSN (Print)0302-9743
ISSN (elektronisch)1611-3349

Konferenz

Konferenz12th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2023
Land/GebietNiederlande
OrtLeiden
Zeitraum20.03.202324.03.2023

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