How Sequential Algorithm Portfolios can benefit Black Box Optimization

TL;DR

This paper demonstrates that sequentially allocating computational budget across multiple algorithms in black-box optimization significantly improves performance over single algorithms, without requiring parallel evaluation.

Contribution

It introduces the concept of sequential algorithm portfolios for black-box optimization and shows their effectiveness using the COCO benchmark, outperforming single algorithms.

Findings

Sequential portfolios outperform single algorithms by over 14%.

Algorithm portfolios benefit from diversity and variance reduction.

No parallel evaluation capabilities are needed for the approach.

Abstract

In typical black-box optimization applications, the available computational budget is often allocated to a single algorithm, typically chosen based on user preference with limited knowledge about the problem at hand or according to some expert knowledge. However, we show that splitting the budget across several algorithms yield significantly better results. This approach benefits from both algorithm complementarity across diverse problems and variance reduction within individual functions, and shows that algorithm portfolios do NOT require parallel evaluation capabilities. To demonstrate the advantage of sequential algorithm portfolios, we apply it to the COCO data archive, using over 200 algorithms evaluated on the BBOB test suite. The proposed sequential portfolios consistently outperform single-algorithm baselines, achieving relative performance gains of over 14%, and offering new insights into restart mechanisms and potential for warm-started execution strategies.