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

Research output: Chapter in Book/Report/Conference proceedingsConference contributionpeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publicationEvolutionary Multi-Criterion Optimization - 12th International Conference, EMO 2023, Proceedings
EditorsMichael Emmerich, André Deutz, Hao Wang, Anna V. Kononova, Boris Naujoks, Ke Li, Kaisa Miettinen, Iryna Yevseyeva
PublisherSpringer
Pages176-190
Number of pages15
ISBN (Print)9783031272493
DOIs
Publication statusPublished - 2023
Event12th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2023 - Leiden, Netherlands
Duration: 20 Mar 202324 Mar 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13970 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference12th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2023
Country/TerritoryNetherlands
CityLeiden
Period20.03.202324.03.2023

Keywords

  • Genetic programming
  • Multi-objective optimization
  • Surrogate model
  • Symbolic regression

Fingerprint

Dive into the research topics of 'Surrogate-assisted Multi-objective Optimization via Genetic Programming Based Symbolic Regression'. Together they form a unique fingerprint.

Cite this