Landscape Features in Single-Objective Continuous Optimization: Have We Hit a Wall in Algorithm Selection Generalization?

TL;DR

This paper investigates the ability of various landscape feature-based algorithm selection models to generalize to unseen problems in continuous optimization, revealing that they often fail to outperform simple baselines on out-of-distribution data.

Contribution

It compares multiple landscape feature representations, including deep learning-based features, in assessing their effectiveness for algorithm selection generalization.

Findings

Feature-based models do not outperform the Single Best Solver on out-of-distribution data.

Deep learning features do not significantly improve generalization.

Current landscape features may have reached a performance plateau.

Abstract

%% 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.