# DynamoRep: Trajectory-Based Population Dynamics for Classification of Black-box Optimization Problems

arXiv: https://arxiv.org/abs/2306.05438 · HTML: https://mlsanity.com/paper/2306.05438/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 33 references that are in the map.
- First posted on arXiv 8 Jun 2023.

## Builds on (most cited first, 14 of 14)

- [Principal Neighbourhood Aggregation for Graph Nets](https://mlsanity.com/paper/2004.05718/) (2020)
- [The CMA Evolution Strategy: A Tutorial](https://mlsanity.com/paper/1604.00772/) (2016)
- [pymoo: Multi-objective Optimization in Python](https://mlsanity.com/paper/2002.04504/) (2020)
- [Why do tree-based models still outperform deep learning on tabular data?](https://mlsanity.com/paper/2207.08815/) (2022)
- [COCO: A Platform for Comparing Continuous Optimizers in a Black-Box Setting](https://mlsanity.com/paper/1603.08785/) (2016)
- [Automated Algorithm Selection: Survey and Perspectives](https://mlsanity.com/paper/1811.11597/) (2018)
- [Pooling Methods in Deep Neural Networks, a Review](https://mlsanity.com/paper/2009.07485/) (2020)
- [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)
- [Per-run Algorithm Selection with Warm-starting using Trajectory-based Features](https://mlsanity.com/paper/2204.09483/) (2022)
- [Towards Feature-Based Performance Regression Using Trajectory Data](https://mlsanity.com/paper/2102.05370/) (2021)
- [Towards Explainable Exploratory Landscape Analysis: Extreme Feature Selection for Classifying BBOB Functions](https://mlsanity.com/paper/2102.00736/) (2021)
- [SELECTOR: Selecting a Representative Benchmark Suite for Reproducible Statistical Comparison](https://mlsanity.com/paper/2204.11527/) (2022)
- [A Collection of Deep Learning-based Feature-Free Approaches for Characterizing Single-Objective Continuous Fitness Landscapes](https://mlsanity.com/paper/2204.05752/) (2022)

## Built on by (most cited first, 8 of 8)

- [On the Utility of Probing Trajectories for Algorithm-Selection](https://mlsanity.com/paper/2401.12745/) (2024)
- [Landscape Features in Single-Objective Continuous Optimization: Have We Hit a Wall in Algorithm Selection Generalization?](https://mlsanity.com/paper/2501.17663/) (2025)
- [A Survey of Features Used for Representing Black-box Single-objective Continuous Optimization](https://mlsanity.com/paper/2406.06629/) (2024)
- [Improving Algorithm-Selection and Performance-Prediction via Learning Discriminating Training Samples](https://mlsanity.com/paper/2404.05359/) (2024)
- [Evolutionary Computation and Explainable AI: A Roadmap to Understandable Intelligent Systems](https://mlsanity.com/paper/2406.07811/) (2024)
- [Identifying Easy Instances to Improve Efficiency of ML Pipelines for Algorithm-Selection](https://mlsanity.com/paper/2406.16999/) (2024)
- [Algorithm Selection with Probing Trajectories: Benchmarking the Choice of Classifier Model](https://mlsanity.com/paper/2501.11414/) (2025)
- [A practical DIRECT-type algorithm for medium-scale black-box global optimization](https://mlsanity.com/paper/2609.09796/) (2026)

## Abstract (arXiv)

The application of machine learning (ML) models to the analysis of optimization algorithms requires the representation of optimization problems using numerical features. These features can be used as input for ML models that are trained to select or to configure a suitable algorithm for the problem at hand. Since in pure black-box optimization information about the problem instance can only be obtained through function evaluation, a common approach is to dedicate some function evaluations for feature extraction, e.g., using random sampling. This approach has two key downsides: (1) It reduces the budget left for the actual optimization phase, and (2) it neglects valuable information that could be obtained from a problem-solver interaction. In this paper, we propose a feature extraction method that describes the trajectories of optimization algorithms using simple descriptive statistics. We evaluate the generated features for the task of classifying problem classes from the Black Box Optimization Benchmarking (BBOB) suite. We demonstrate that the proposed DynamoRep features capture enough information to identify the problem class on which the optimization algorithm is running, achieving a mean classification accuracy of 95% across all experiments.