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

arXiv: https://arxiv.org/abs/2406.06629 · HTML: https://mlsanity.com/paper/2406.06629/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 46 of its 130 references that are in the map.
- First posted on arXiv 8 Jun 2024.

## Builds on (most cited first, 30 of 46)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Dynamic Graph CNN for Learning on Point Clouds](https://mlsanity.com/paper/1801.07829/) (2018)
- [An Introduction to Variational Autoencoders](https://mlsanity.com/paper/1906.02691/) (2019)
- [Revisiting Deep Learning Models for Tabular Data](https://mlsanity.com/paper/2106.11959/) (2021)
- [Recent Advances in Convolutional Neural Networks](https://mlsanity.com/paper/1512.07108/) (2015)
- [An introduction to Topological Data Analysis: fundamental and practical aspects for data scientists](https://mlsanity.com/paper/1710.04019/) (2017)
- [A Random Forest Guided Tour](https://mlsanity.com/paper/1511.05741/) (2015)
- [Automated Algorithm Selection: Survey and Perspectives](https://mlsanity.com/paper/1811.11597/) (2018)
- [Comprehensive Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems Using the R-Package flacco](https://mlsanity.com/paper/1708.05258/) (2017)
- [Exploratory Landscape Analysis is Strongly Sensitive to the Sampling Strategy](https://mlsanity.com/paper/2006.11135/) (2020)
- [Tuning as a Means of Assessing the Benefits of New Ideas in Interplay with Existing Algorithmic Modules](https://mlsanity.com/paper/2102.12905/) (2021)
- [Per-run Algorithm Selection with Warm-starting using Trajectory-based Features](https://mlsanity.com/paper/2204.09483/) (2022)
- [Landscape-Aware Fixed-Budget Performance Regression and Algorithm Selection for Modular CMA-ES Variants](https://mlsanity.com/paper/2006.09855/) (2020)
- [Towards Feature-Based Performance Regression Using Trajectory Data](https://mlsanity.com/paper/2102.05370/) (2021)
- [DoE2Vec: Deep-learning Based Features for Exploratory Landscape Analysis](https://mlsanity.com/paper/2304.01219/) (2023)
- [Local Optima Networks: A New Model of Combinatorial Fitness Landscapes](https://mlsanity.com/paper/1402.2959/) (2014)
- [Evolving the Structure of Evolution Strategies](https://mlsanity.com/paper/1610.05231/) (2016)
- [The Impact of Hyper-Parameter Tuning for Landscape-Aware Performance Regression and Algorithm Selection](https://mlsanity.com/paper/2104.09272/) (2021)
- [SELECTOR: Selecting a Representative Benchmark Suite for Reproducible Statistical Comparison](https://mlsanity.com/paper/2204.11527/) (2022)
- [Modular Differential Evolution](https://mlsanity.com/paper/2304.09524/) (2023)
- [MA-BBOB: A Problem Generator for Black-Box Optimization Using Affine Combinations and Shifts](https://mlsanity.com/paper/2312.11083/) (2023)
- [HPO X ELA: Investigating Hyperparameter Optimization Landscapes by Means of Exploratory Landscape Analysis](https://mlsanity.com/paper/2208.00220/) (2022)
- [Comparing Algorithm Selection Approaches on Black-Box Optimization Problems](https://mlsanity.com/paper/2306.17585/) (2023)
- [Explainable Benchmarking for Iterative Optimization Heuristics](https://mlsanity.com/paper/2401.17842/) (2024)
- [To Switch or not to Switch: Predicting the Benefit of Switching between Algorithms based on Trajectory Features](https://mlsanity.com/paper/2302.09075/) (2023)
- [A Collection of Deep Learning-based Feature-Free Approaches for Characterizing Single-Objective Continuous Fitness Landscapes](https://mlsanity.com/paper/2204.05752/) (2022)
- [Trajectory-based Algorithm Selection with Warm-starting](https://mlsanity.com/paper/2204.06397/) (2022)
- [DynamoRep: Trajectory-Based Population Dynamics for Classification of Black-box Optimization Problems](https://mlsanity.com/paper/2306.05438/) (2023)

## Built on by (most cited first, 3 of 3)

- [A Systematic Survey on Large Language Models for Evolutionary Optimization: From Modeling to Solving](https://mlsanity.com/paper/2509.08269/) (2025)
- [Geometric Probing for Algorithm Selection in Continuous Black-Box Optimization](https://mlsanity.com/paper/2604.09095/) (2026)
- [On the Influence of the Feature Computation Budget on Per-Instance Algorithm Selection for Black-Box Optimization](https://mlsanity.com/paper/2605.04954/) (2026)

## Abstract (arXiv)

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.