# Improving Nevergrad's Algorithm Selection Wizard NGOpt through Automated Algorithm Configuration

arXiv: https://arxiv.org/abs/2209.04412 · HTML: https://mlsanity.com/paper/2209.04412/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Sep 2022.

## Built on by (most cited first, 4 of 4)

- [Explainable Benchmarking for Iterative Optimization Heuristics](https://mlsanity.com/paper/2401.17842/) (2024)
- [Hybrid Quantum Graph Neural Network for Molecular Property Prediction](https://mlsanity.com/paper/2405.05205/) (2024)
- [Explainable Information Processing in Particle Swarm Optimization through Landscape and Search Behavior Analysis](https://mlsanity.com/paper/2509.06272/) (2025)
- [A New Approach to Characterising Optimisation Problems Using Programmatic Representation and Complexity Measures](https://mlsanity.com/paper/2608.08898/) (2026)

## Abstract (arXiv)

Algorithm selection wizards are effective and versatile tools that automatically select an optimization algorithm given high-level information about the problem and available computational resources, such as number and type of decision variables, maximal number of evaluations, possibility to parallelize evaluations, etc. State-of-the-art algorithm selection wizards are complex and difficult to improve. We propose in this work the use of automated configuration methods for improving their performance by finding better configurations of the algorithms that compose them. In particular, we use elitist iterated racing (irace) to find CMA configurations for specific artificial benchmarks that replace the hand-crafted CMA configurations currently used in the NGOpt wizard provided by the Nevergrad platform. We discuss in detail the setup of irace for the purpose of generating configurations that work well over the diverse set of problem instances within each benchmark. Our approach improves the performance of the NGOpt wizard, even on benchmark suites that were not part of the tuning by irace.