Accelerating surrogate assisted evolutionary algorithms for expensive multi-objective optimization via explainable machine learning

Bingdong LI, Yanting YANG, Dacheng LIU, Yan ZHANG, Aimin ZHOU, Xin YAO

Research output: Journal PublicationsJournal Article (refereed)peer-review

Abstract

A series of surrogate-assisted evolutionary algorithms (SAEAs) have been proposed to handle expensive multi-objective optimization problems (EMOPs). However, the surrogate of these SAEAs is underutilized to a large extent, which limits the search efficiency of these algorithms. To be specific, existing algorithms do not sufficiently exploit the estimated solution quality information from the surrogate models during offspring generation. To address this issue, this paper proposes an SAEA framework named EXO-SAEA (EXplanation Operator based Surrogate-Assisted Evolutionary Algorithm). First, it divides the current population into two populations according to the a priori knowledge from the surrogate model. Then, for each solution in the first population, EXO-SAEA employs the SHapley Additive exPlanations (SHAP) model to estimate the contribution of each decision variable to the fitness values. After that, the Shapley values are then normalized for the offspring generation of the first population, while the second population uses generic GA operators. Two representative surrogate-assisted evolutionary algorithms are used to instantiate the proposed framework. Experimental results on the synthetic benchmark problems and three real-world problems involving six state-of-the-art algorithms demonstrate the effectiveness of the proposed framework.
Original languageEnglish
Article number101610
JournalSwarm and Evolutionary Computation
Volume88
Early online date31 May 2024
DOIs
Publication statusE-pub ahead of print - 31 May 2024

Bibliographical note

Publisher Copyright:
© 2024 Elsevier B.V.

Keywords

  • Crossover operator
  • Expensive optimization
  • Explainable machine learning
  • Multi-objective optimization
  • Surrogate-assisted evolutionary algorithm

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