Surrogate Ensemble in Expensive Multi-Objective Optimization via Deep Q-Learning

• URL: http://arxiv.org/abs/2602.00540v1
• Date: Sat, 31 Jan 2026 06:14:27 GMT
• Title: Surrogate Ensemble in Expensive Multi-Objective Optimization via Deep Q-Learning
• Authors: Yuxin Wu, Hongshu Guo, Ting Huang, Yue-Jiao Gong, Zeyuan Ma,
• Abstract summary: Surrogate-assisted Evolutionary Algorithms(SAEAs) have shown promising robustness in solving expensive optimization problems.<n>A key aspect that impacts SAEAs' effectiveness is surrogate model selection, which in existing works is predominantly decided by human developer.<n>We propose a reinforcement learning-assisted ensemble framework, termed as SEEMOO, which is capable of scheduling different surrogate models within a single optimization process.
• Score: 17.84264663466905
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Surrogate-assisted Evolutionary Algorithms~(SAEAs) have shown promising robustness in solving expensive optimization problems. A key aspect that impacts SAEAs' effectiveness is surrogate model selection, which in existing works is predominantly decided by human developer. Such human-made design choice introduces strong bias into SAEAs and may hurt their expected performance on out-of-scope tasks. In this paper, we propose a reinforcement learning-assisted ensemble framework, termed as SEEMOO, which is capable of scheduling different surrogate models within a single optimization process, hence boosting the overall optimization performance in a cooperative paradigm. Specifically, we focus on expensive multi-objective optimization problems, where multiple objective functions shape a compositional landscape and hence challenge surrogate selection. SEEMOO comprises following core designs: 1) A pre-collected model pool that maintains different surrogate models; 2) An attention-based state-extractor supports universal optimization state representation of problems with varied objective numbers; 3) a deep Q-network serves as dynamic surrogate selector: Given the optimization state, it selects desired surrogate model for current-step evaluation. SEEMOO is trained to maximize the overall optimization performance under a training problem distribution. Extensive benchmark results demonstrate SEEMOO's surrogate ensemble paradigm boosts the optimization performance of single-surrogate baselines. Further ablation studies underscore the importance of SEEMOO's design components.

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