A Survey of Meta-features Used for Automated Selection of Algorithms for Black-box Single-objective Continuous Optimization

TL;DR

This survey reviews the use of meta-features and machine learning models for automated algorithm selection in black-box single-objective continuous optimization, highlighting current approaches, challenges, and future directions.

Contribution

It provides a comprehensive overview of meta-feature representations and machine learning techniques in algorithm selection for continuous optimization, identifying gaps and proposing future research directions.

Findings

Meta-feature representation learning is an active research area.

Machine learning models are effective for algorithm performance prediction.

Gaps exist in current meta-feature and model integration methods.

Abstract

The selection of the most appropriate algorithm to solve a given problem instance, known as algorithm selection, is driven by the potential to capitalize on the complementary performance of different algorithms across sets of problem instances. However, determining the optimal algorithm for an unseen problem instance has been shown to be a challenging task, which has garnered significant attention from researchers in recent years. In this survey, we conduct an overview of the key contributions to algorithm selection in the field of single-objective continuous black-box optimization. We present ongoing work in representation learning of meta-features for optimization problem instances, algorithm instances, and their interactions. We also study machine learning models for automated algorithm selection, configuration, and performance prediction. Through this analysis, we identify gaps in the state of the art, based on which we present ideas for further development of meta-feature representations.