# Evolving the Structure of Evolution Strategies

arXiv: https://arxiv.org/abs/1610.05231 · HTML: https://mlsanity.com/paper/1610.05231/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Oct 2016.

## Built on by (most cited first, 11 of 11)

- [LLaMEA: A Large Language Model Evolutionary Algorithm for Automatically Generating Metaheuristics](https://mlsanity.com/paper/2405.20132/) (2024)
- [Automated Design of Metaheuristic Algorithms: A Survey](https://mlsanity.com/paper/2303.06532/) (2023)
- [The Importance of Landscape Features for Performance Prediction of Modular CMA-ES Variants](https://mlsanity.com/paper/2204.07431/) (2022)
- [DesignX: Human-Competitive Algorithm Designer for Black-Box Optimization](https://mlsanity.com/paper/2505.17866/) (2025)
- [Automated Benchmark-Driven Design and Explanation of Hyperparameter Optimizers](https://mlsanity.com/paper/2111.14756/) (2021)
- [Behaviour Space Analysis of LLM-driven Meta-heuristic Discovery](https://mlsanity.com/paper/2507.03605/) (2025)
- [Evolving Continuous Optimisers from Scratch](https://mlsanity.com/paper/2103.11746/) (2021)
- [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)
- [From Performance to Understanding: A Vision for Explainable Automated Algorithm Design](https://mlsanity.com/paper/2511.16201/) (2025)
- [Geometric Probing for Algorithm Selection in Continuous Black-Box Optimization](https://mlsanity.com/paper/2604.09095/) (2026)

## Abstract (arXiv)

Various variants of the well known Covariance Matrix Adaptation Evolution Strategy (CMA-ES) have been proposed recently, which improve the empirical performance of the original algorithm by structural modifications. However, in practice it is often unclear which variation is best suited to the specific optimization problem at hand. As one approach to tackle this issue, algorithmic mechanisms attached to CMA-ES variants are considered and extracted as functional modules, allowing for combinations of them. This leads to a configuration space over ES structures, which enables the exploration of algorithm structures and paves the way toward novel algorithm generation. Specifically, eleven modules are incorporated in this framework with two or three alternative configurations for each module, resulting in 4,608 algorithms. A self-adaptive Genetic Algorithm (GA) is used to efficiently evolve effective ES-structures for given classes of optimization problems, outperforming any classical CMA-ES variants from literature. The proposed approach is evaluated on noiseless functions from BBOB suite. Furthermore, such an observation is again confirmed on different function groups and dimensionality, indicating the feasibility of ES configuration on real-world problem classes.