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 language | English |
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Article number | 101610 |
Journal | Swarm and Evolutionary Computation |
Volume | 88 |
Early online date | 31 May 2024 |
DOIs | |
Publication status | Published - Jul 2024 |
Bibliographical note
Publisher Copyright:© 2024 Elsevier B.V.
Funding
This work was supported by the National Natural Science Foundation of China (Grant No. 62250710682), the Guangdong Provincial Key Laboratory (Grant No. 2020B121201001), and the Program for Guangdong Introducing Innovative and Entrepreneurial Teams (Grant No. 2017ZT07X386).
Keywords
- Crossover operator
- Expensive optimization
- Explainable machine learning
- Multi-objective optimization
- Surrogate-assisted evolutionary algorithm