Benchmarking Tabular Foundation Models as Surrogates in Expensive Evolutionary Optimization

2026-09-16

AI Summary

研究使用TabPFN作为昂贵优化问题中替代模型的有效性，通过实验和理论分析其在不同场景下的优劣，并提出应用指南。

Abstract

Surrogate-assisted evolutionary algorithms (SAEAs) are effective methods for solving expensive optimization problems (EOPs), where surrogate models replace most expensive evaluations and critically influence the final optimization results. In recent years, tabular foundation models have advanced rapidly, and the Tabular Prior-data Fitted Network (TabPFN) has been adopted as a surrogate model for EOPs due to its strong predictive capability, demonstrating promising performance. Motivated by its potential as a surrogate model in SAEAs, this work conducts a comprehensive study that combines extensive experiments with in-depth theoretical analysis to investigate the effectiveness of TabPFN. Specifically, we perform experiments across both offline and online SAEA settings, covering diverse problem scenarios such as single-objective, multi-objective, constrained, combinatorial, mixed-variable, and engineering optimization problems. In addition, we further analyze the advantages and limitations of TabPFN within SAEAs and provide practical guidelines for its application in different optimization settings. Results show that the effectiveness of TabPFN is highly problem dependent, and it cannot replace conventional surrogates universally. Overall, TabPFN should be adopted selectively according to data availability, landscape complexity, search space characteristics, and its role within the algorithm. Customized model management strategies and role-specific algorithm design are necessary to fully exploit its advantages and avoid its pitfalls.

Problem

• Surrogate-assisted evolutionary algorithms
• Expensive optimization problems
• TabPFN
• Problem dependency
• Optimization settings

Innovation

• TabPFN
• Surrogate-assisted evolutionary algorithms
• Expensive optimization problems
• Customized model management strategies