# Comprehensive Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems Using the R-Package flacco

arXiv: https://arxiv.org/abs/1708.05258 · HTML: https://mlsanity.com/paper/1708.05258/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Aug 2017.

## Built on by (most cited first, 27 of 27)

- [Towards Explainable Exploratory Landscape Analysis: Extreme Feature Selection for Classifying BBOB Functions](https://mlsanity.com/paper/2102.00736/) (2021)
- [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)
- [Neural Exploratory Landscape Analysis for Meta-Black-Box-Optimization](https://mlsanity.com/paper/2408.10672/) (2024)
- [Deep-ELA: Deep Exploratory Landscape Analysis with Self-Supervised Pretrained Transformers for Single- and Multi-Objective Continuous Optimization Problems](https://mlsanity.com/paper/2401.01192/) (2024)
- [A Collection of Deep Learning-based Feature-Free Approaches for Characterizing Single-Objective Continuous Fitness Landscapes](https://mlsanity.com/paper/2204.05752/) (2022)
- [DynamoRep: Trajectory-Based Population Dynamics for Classification of Black-box Optimization Problems](https://mlsanity.com/paper/2306.05438/) (2023)
- [Explainable Landscape-Aware Optimization Performance Prediction](https://mlsanity.com/paper/2110.11633/) (2021)
- [Generalization Ability of Feature-based Performance Prediction Models: A Statistical Analysis across Benchmarks](https://mlsanity.com/paper/2405.12259/) (2024)
- [A Survey of Features Used for Representing Black-box Single-objective Continuous Optimization](https://mlsanity.com/paper/2406.06629/) (2024)
- [Automated Algorithm Selection for Radar Network Configuration](https://mlsanity.com/paper/2205.03670/) (2022)
- [Towards Automated Design of Bayesian Optimization via Exploratory Landscape Analysis](https://mlsanity.com/paper/2211.09678/) (2022)
- [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)
- [Towards the Inferrence of Structural Similarity of Combinatorial Landscapes](https://mlsanity.com/paper/2312.02720/) (2023)
- [Landscape-Aware Automated Algorithm Configuration using Multi-output Mixed Regression and Classification](https://mlsanity.com/paper/2409.01446/) (2024)
- [Meta-Black-Box Optimization with Ensemble Surrogate Modeling for Robustness-Accuracy Trade-off within SAEA](https://mlsanity.com/paper/2606.00862/) (2026)
- [Landscape Analysis for Surrogate Models in the Evolutionary Black-Box Context](https://mlsanity.com/paper/2203.11315/) (2022)
- [OPTION: OPTImization Algorithm Benchmarking ONtology](https://mlsanity.com/paper/2211.11332/) (2022)
- [Characterising harmful data sources when constructing multi-fidelity surrogate models](https://mlsanity.com/paper/2403.08118/) (2024)
- [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)
- [Hilbert curves for efficient exploratory landscape analysis neighbourhood sampling](https://mlsanity.com/paper/2408.00526/) (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)
- [ASAP: Agent-System Co-Design for Wall-Clock-Centered Auto HPO Research for ML Experiments](https://mlsanity.com/paper/2606.25207/) (2026)
- [A New Approach to Characterising Optimisation Problems Using Programmatic Representation and Complexity Measures](https://mlsanity.com/paper/2608.08898/) (2026)
- [Hyper Algorithm Design Agent: Evolving Learnable Optimizer from Zero](https://mlsanity.com/paper/2609.35328/) (2026)

## Abstract (arXiv)

Choosing the best-performing optimizer(s) out of a portfolio of optimization algorithms is usually a difficult and complex task. It gets even worse, if the underlying functions are unknown, i.e., so-called Black-Box problems, and function evaluations are considered to be expensive. In the case of continuous single-objective optimization problems, Exploratory Landscape Analysis (ELA) - a sophisticated and effective approach for characterizing the landscapes of such problems by means of numerical values before actually performing the optimization task itself - is advantageous. Unfortunately, until now it has been quite complicated to compute multiple ELA features simultaneously, as the corresponding code has been - if at all - spread across multiple platforms or at least across several packages within these platforms. This article presents a broad summary of existing ELA approaches and introduces flacco, an R-package for feature-based landscape analysis of continuous and constrained optimization problems. Although its functions neither solve the optimization problem itself nor the related "Algorithm Selection Problem (ASP)", it offers easy access to an essential ingredient of the ASP by providing a wide collection of ELA features on a single platform - even within a single package. In addition, flacco provides multiple visualization techniques, which enhance the understanding of some of these numerical features, and thereby make certain landscape properties more comprehensible. On top of that, we will introduce the package's build-in, as well as web-hosted and hence platform-independent, graphical user interface (GUI), which facilitates the usage of the package - especially for people who are not familiar with R - making it a very convenient toolbox when working towards algorithm selection of continuous single-objective optimization problems.