Benchmarking Feature-based Algorithm Selection Systems for Black-box Numerical Optimization

TL;DR

This paper evaluates feature-based algorithm selection systems for black-box numerical optimization, analyzing benchmarking methodologies, the impact of randomness, and the effectiveness of pre-solvers to improve performance.

Contribution

It provides a comprehensive benchmarking methodology, compares performance measures, and investigates factors influencing algorithm selection effectiveness in black-box optimization.

Findings

First performance measure is more reliable than expected runtime.

Randomness significantly affects algorithm selection performance.

Sequential least squares programming improves selection outcomes.

Abstract

Feature-based algorithm selection aims to automatically find the best one from a portfolio of optimization algorithms on an unseen problem based on its landscape features. Feature-based algorithm selection has recently received attention in the research field of black-box numerical optimization. However, there is still room for analysis of algorithm selection for black-box optimization. Most previous studies have focused only on whether an algorithm selection system can outperform the single-best solver in a portfolio. In addition, a benchmarking methodology for algorithm selection systems has not been well investigated in the literature. In this context, this paper analyzes algorithm selection systems on the 24 noiseless black-box optimization benchmarking functions. First, we demonstrate that the first successful performance measure is more reliable than the expected runtime measure for benchmarking algorithm selection systems. Then, we examine the influence of randomness on the performance of algorithm selection systems. We also show that the performance of algorithm selection systems can be significantly improved by using sequential least squares programming as a pre-solver. We point out that the difficulty of outperforming the single-best solver depends on algorithm portfolios, cross-validation methods, and dimensions. Finally, we demonstrate that the effectiveness of algorithm portfolios depends on various factors. These findings provide fundamental insights for algorithm selection for black-box optimization.