# Automated Algorithm Selection on Continuous Black-Box Problems By Combining Exploratory Landscape Analysis and Machine Learning

arXiv: https://arxiv.org/abs/1711.08921 · HTML: https://mlsanity.com/paper/1711.08921/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Nov 2017.

## Built on by (most cited first, 30 of 33)

- [Toward Automated Algorithm Design: A Survey and Practical Guide to Meta-Black-Box-Optimization](https://mlsanity.com/paper/2411.00625/) (2024)
- [Towards Explainable Exploratory Landscape Analysis: Extreme Feature Selection for Classifying BBOB Functions](https://mlsanity.com/paper/2102.00736/) (2021)
- [Automated Algorithm Selection: from Feature-Based to Feature-Free Approaches](https://mlsanity.com/paper/2203.13392/) (2022)
- [DoE2Vec: Deep-learning Based Features for Exploratory Landscape Analysis](https://mlsanity.com/paper/2304.01219/) (2023)
- [HPO X ELA: Investigating Hyperparameter Optimization Landscapes by Means of Exploratory Landscape Analysis](https://mlsanity.com/paper/2208.00220/) (2022)
- [Explainable Benchmarking for Iterative Optimization Heuristics](https://mlsanity.com/paper/2401.17842/) (2024)
- [Neural Exploratory Landscape Analysis for Meta-Black-Box-Optimization](https://mlsanity.com/paper/2408.10672/) (2024)
- [A Collection of Deep Learning-based Feature-Free Approaches for Characterizing Single-Objective Continuous Fitness Landscapes](https://mlsanity.com/paper/2204.05752/) (2022)
- [Explainable Landscape-Aware Optimization Performance Prediction](https://mlsanity.com/paper/2110.11633/) (2021)
- [PS-AAS: Portfolio Selection for Automated Algorithm Selection in Black-Box Optimization](https://mlsanity.com/paper/2310.10685/) (2023)
- [Assessing the Generalizability of a Performance Predictive Model](https://mlsanity.com/paper/2306.00040/) (2023)
- [Generalization Ability of Feature-based Performance Prediction Models: A Statistical Analysis across Benchmarks](https://mlsanity.com/paper/2405.12259/) (2024)
- [Explainable Landscape Analysis in Automated Algorithm Performance Prediction](https://mlsanity.com/paper/2203.11828/) (2022)
- [The Pitfalls of Benchmarking in Algorithm Selection: What We Are Getting Wrong](https://mlsanity.com/paper/2505.07750/) (2025)
- [Tools for Landscape Analysis of Optimisation Problems in Procedural Content Generation for Games](https://mlsanity.com/paper/2302.08479/) (2023)
- [Computational and Exploratory Landscape Analysis of the GKLS Generator](https://mlsanity.com/paper/2304.08913/) (2023)
- [Sensitivity Analysis of RF+clust for Leave-one-problem-out Performance Prediction](https://mlsanity.com/paper/2305.19375/) (2023)
- [Towards the Inferrence of Structural Similarity of Combinatorial Landscapes](https://mlsanity.com/paper/2312.02720/) (2023)
- [Identifying Easy Instances to Improve Efficiency of ML Pipelines for Algorithm-Selection](https://mlsanity.com/paper/2406.16999/) (2024)
- [Landscape-Aware Automated Algorithm Configuration using Multi-output Mixed Regression and Classification](https://mlsanity.com/paper/2409.01446/) (2024)
- [Beyond Numerical Features: CNN-Driven Algorithm Selection via Contour Plots for Continuous Black-Box Optimization](https://mlsanity.com/paper/2605.20797/) (2026)
- [Toward an ImageNet Library of Functions for Global Optimization Benchmarking](https://mlsanity.com/paper/2206.13630/) (2022)
- [On Constructing Algorithm Portfolios in Algorithm Selection for Computationally Expensive Black-box Optimization in the Fixed-budget Setting](https://mlsanity.com/paper/2405.10976/) (2024)
- [Greedy Restart Schedules: A Baseline for Dynamic Algorithm Selection on Numerical Black-box Optimization Problems](https://mlsanity.com/paper/2504.11440/) (2025)
- [Explainable Information Processing in Particle Swarm Optimization through Landscape and Search Behavior Analysis](https://mlsanity.com/paper/2509.06272/) (2025)
- [From Performance to Understanding: A Vision for Explainable Automated Algorithm Design](https://mlsanity.com/paper/2511.16201/) (2025)
- [OPAL: Operator-Programmed Algorithms for Landscape-Aware Black-Box Optimization](https://mlsanity.com/paper/2512.12809/) (2025)
- [Geometric Probing for Algorithm Selection in Continuous Black-Box Optimization](https://mlsanity.com/paper/2604.09095/) (2026)
- [Does Dimensionality Reduction via Random Projections Preserve Landscape Features?](https://mlsanity.com/paper/2604.13230/) (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)

In this paper, we build upon previous work on designing informative and efficient Exploratory Landscape Analysis features for characterizing problems' landscapes and show their effectiveness in automatically constructing algorithm selection models in continuous black-box optimization problems. Focussing on algorithm performance results of the COCO platform of several years, we construct a representative set of high-performing complementary solvers and present an algorithm selection model that - compared to the portfolio's single best solver - on average requires less than half of the resources for solving a given problem. Therefore, there is a huge gain in efficiency compared to classical ensemble methods combined with an increased insight into problem characteristics and algorithm properties by using informative features. Acting on the assumption that the function set of the Black-Box Optimization Benchmark is representative enough for practical applications the model allows for selecting the best suited optimization algorithm within the considered set for unseen problems prior to the optimization itself based on a small sample of function evaluations. Note that such a sample can even be reused for the initial population of an evolutionary (optimization) algorithm so that even the feature costs become negligible.