基准测试表格基础模型作为昂贵进化优化中的代理模型

中文摘要 AI 辅助

代理辅助进化算法（SAEAs）是解决昂贵优化问题（EOPs）的有效方法，其中代理模型替代大部分昂贵的评估，并关键性地影响最终优化结果。近年来，表格基础模型快速发展，表格先验数据拟合网络（TabPFN）因其强大的预测能力而被用作EOPs的代理模型，展现出有前景的性能。受其作为SAEAs中代理模型潜力的启发，本研究进行了全面研究，结合大量实验与深入的理论分析，以探究TabPFN的有效性。具体而言，我们在离线和在线SAEA设置下进行了实验，覆盖了多种问题场景，如单目标、多目标、约束、组合、混合变量和工程优化问题。此外，我们进一步分析了TabPFN在SAEAs中的优势与局限性，并为其在不同优化设置中的应用提供了实用指南。结果表明，TabPFN的有效性高度依赖于问题，它不能普遍替代传统代理模型。总体而言，应根据数据可用性、景观复杂性、搜索空间特征及其在算法中的角色，有选择地采用TabPFN。定制的模型管理策略和角色特定的算法设计对于充分利用其优势并避免其缺陷是必要的。

英文摘要

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.

发表机构

• Xidian University（西安电子科技大学）

机构由 AI 辅助整理，请以论文原文为准。