现有优化算法选择模型在新问题上表现不佳，甚至不如单一最佳求解器。
Landscape Features in Single-Objective Continuous Optimization: Have We Hit a Wall in Algorithm Selection Generalization?
- 对比多种问题特征表示的算法选择模型
- 在分布外数据上均无法超越简单基准模型
- 揭示算法选择泛化能力存在瓶颈，适合关注优化系统鲁棒性的研究者
算法选择（AS）旨在通过问题景观特征预测不同优化算法的性能，从而为特定问题挑选最优算法。本研究评估了单目标连续优化中不同问题表示方式下AS模型的泛化能力，涵盖广泛使用的探索性景观分析（Exploratory Landscape Analysis, ELA）特征、新兴的拓扑景观分析（Topological Landscape Analysis）特征，以及基于深度学习的DeepELA、TransOptAS和Doe2Vec等特征。结果表明，在面对分布外评估数据时，所有基于特征的AS模型均未能超越简单的基准模型——即单一最佳求解器。这暗示当前算法选择方法在泛化能力上可能已触及瓶颈。
原文摘要 · Abstract (English)
%% Text of abstract The process of identifying the most suitable optimization algorithm for a specific problem, referred to as algorithm selection (AS), entails training models that leverage problem landscape features to forecast algorithm performance. A significant challenge in this domain is ensuring that AS models can generalize effectively to novel, unseen problems. This study evaluates the generalizability of AS models based on different problem representations in the context of single-objective continuous optimization. In particular, it considers the most widely used Exploratory Landscape Analysis features, as well as recently proposed Topological Landscape Analysis features, and features based on deep learning, such as DeepELA, TransOptAS and Doe2Vec. Our results indicate that when presented with out-of-distribution evaluation data, none of the feature-based AS models outperform a simple baseline model, i.e., a Single Best Solver.
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