A Survey of Features Used for Representing Black-box Single-objective Continuous Optimization

Abstract

This survey examines key advancements in designing features to represent optimization problem instances, algorithm instances, and their interactions within the context of single-objective continuous black-box optimization. These features support machine learning tasks such as algorithm selection, algorithm configuration, and problem classification, and they are also used to evaluate the complementarity of benchmark problem sets. We provide a comprehensive overview of problem landscape features, algorithm features, high-level problem-algorithm interaction features, and trajectory features, including the latest works from the past five years. We also point out limitations of the current state-of-the-art and suggest directions for future research.

66 pages

References in corpus (6)

• XGBoost: A Scalable Tree Boosting System
• An Introduction to Variational Autoencoders
• Landscape-Aware Fixed-Budget Performance Regression and Algorithm Selection for Modular CMA-ES Variants
• The Impact of Hyper-Parameter Tuning for Landscape-Aware Performance Regression and Algorithm Selection
• Benchmarking Feature-based Algorithm Selection Systems for Black-box Numerical Optimization
• A Complementarity Analysis of the COCO Benchmark Problems and Artificially Generated Problems