mlsanityData to 14 Oct 2026

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

arXiv 2406.06629 ↗First posted 8 Jun 2024cs.LG cs.NEcited by 3

Not on the map. Why?

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.

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

46 of 130 references.

References · 46

  1. 2017Attention Is All You Needcited by 60,361
  2. 2017A Unified Approach to Interpreting Model Predictionscited by 6,555
  3. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,166
  4. 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,223
  5. 2019An Introduction to Variational Autoencoderscited by 1,000
  6. 2021Revisiting Deep Learning Models for Tabular Datacited by 740
  7. 2015Recent Advances in Convolutional Neural Networkscited by 360
  8. 2017An introduction to Topological Data Analysis: fundamental and practical aspects for data scientistscited by 172
  9. 2015A Random Forest Guided Tourcited by 168
  10. 2018Automated Algorithm Selection: Survey and Perspectivescited by 114
  11. 2017Comprehensive Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems Using the R-Package flaccocited by 27
  12. 2020Exploratory Landscape Analysis is Strongly Sensitive to the Sampling Strategycited by 27
34 more
  1. 2021Tuning as a Means of Assessing the Benefits of New Ideas in Interplay with Existing Algorithmic Modulescited by 20
  2. 2022Per-run Algorithm Selection with Warm-starting using Trajectory-based Featurescited by 20
  3. 2020Landscape-Aware Fixed-Budget Performance Regression and Algorithm Selection for Modular CMA-ES Variantscited by 15
  4. 2021Towards Feature-Based Performance Regression Using Trajectory Datacited by 15
  5. 2023DoE2Vec: Deep-learning Based Features for Exploratory Landscape Analysiscited by 12
  6. 2014Local Optima Networks: A New Model of Combinatorial Fitness Landscapescited by 11
  7. 2016Evolving the Structure of Evolution Strategiescited by 11
  8. 2021The Impact of Hyper-Parameter Tuning for Landscape-Aware Performance Regression and Algorithm Selectioncited by 11
  9. 2022SELECTOR: Selecting a Representative Benchmark Suite for Reproducible Statistical Comparisoncited by 11
  10. 2023Modular Differential Evolutioncited by 11
  11. 2023MA-BBOB: A Problem Generator for Black-Box Optimization Using Affine Combinations and Shiftscited by 11
  12. 2022HPO X ELA: Investigating Hyperparameter Optimization Landscapes by Means of Exploratory Landscape Analysiscited by 10
  13. 2023Comparing Algorithm Selection Approaches on Black-Box Optimization Problemscited by 10
  14. 2024Explainable Benchmarking for Iterative Optimization Heuristicscited by 10
  15. 2023To Switch or not to Switch: Predicting the Benefit of Switching between Algorithms based on Trajectory Featurescited by 9
  16. 2022A Collection of Deep Learning-based Feature-Free Approaches for Characterizing Single-Objective Continuous Fitness Landscapescited by 8
  17. 2022Trajectory-based Algorithm Selection with Warm-startingcited by 8
  18. 2023DynamoRep: Trajectory-Based Population Dynamics for Classification of Black-box Optimization Problemscited by 8
  19. 2024On the Utility of Probing Trajectories for Algorithm-Selectioncited by 8
  20. 2021Benchmarking Feature-based Algorithm Selection Systems for Black-box Numerical Optimizationcited by 7
  21. 2021Explorative Data Analysis of Time Series based AlgorithmFeatures of CMA-ES Variantscited by 6
  22. 2022The Importance of Landscape Features for Performance Prediction of Modular CMA-ES Variantscited by 5
  23. 2024Impact of Training Instance Selection on Automated Algorithm Selection Models for Numerical Black-box Optimizationcited by 5
  24. 2020Linear Matrix Factorization Embeddings for Single-objective Optimization Landscapescited by 4
  25. 2023Using Knowledge Graphs for Performance Prediction of Modular Optimization Algorithmscited by 4
  26. 2023Assessing the Generalizability of a Performance Predictive Modelcited by 4
  27. 2020Neural Network Design: Learning from Neural Architecture Searchcited by 3
  28. 2022Explainable Landscape Analysis in Automated Algorithm Performance Predictioncited by 3
  29. 2023RF+clust for Leave-One-Problem-Out Performance Predictioncited by 3
  30. 2023Challenges of ELA-guided Function Evolution using Genetic Programmingcited by 3
  31. 2021A Complementarity Analysis of the COCO Benchmark Problems and Artificially Generated Problemscited by 2
  32. 2023Computational and Exploratory Landscape Analysis of the GKLS Generatorcited by 2
  33. 2023Sensitivity Analysis of RF+clust for Leave-one-problem-out Performance Predictioncited by 2
  34. 2023Algorithm Instance Footprint: Separating Easily Solvable and Challenging Problem Instancescited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025A Systematic Survey on Large Language Models for Evolutionary Optimization: From Modeling to Solvingcited by 11
  2. 2026Geometric Probing for Algorithm Selection in Continuous Black-Box Optimizationcited by 0
  3. 2026On the Influence of the Feature Computation Budget on Per-Instance Algorithm Selection for Black-Box Optimizationcited by 0