# Robust Optimization through Neuroevolution

arXiv: https://arxiv.org/abs/1810.01125 · HTML: https://mlsanity.com/paper/1810.01125/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Oct 2018.

## Built on by (most cited first, 3 of 3)

- [The Role of Morphological Variation in Evolutionary Robotics: Maximizing Performance and Robustness](https://mlsanity.com/paper/2208.02809/) (2022)
- [Qualitative Differences Between Evolutionary Strategies and Reinforcement Learning Methods for Control of Autonomous Agents](https://mlsanity.com/paper/2205.07592/) (2022)
- [Learning and evolution: factors influencing an effective combination](https://mlsanity.com/paper/2306.11761/) (2023)

## Abstract (arXiv)

We propose a method for evolving solutions that are robust with respect to variations of the environmental conditions (i.e. that can operate effectively in new conditions immediately, without the need to adapt to variations). The obtained results show how the method proposed is effective and computational tractable. It permits to improve performance on an extended version of the double-pole balancing problem, to outperform the best available human-designed controllers on a car racing problem, and to generate rather effective solutions for a swarm robotic problem. The comparison of different algorithms indicates that the CMA-ES and xNES methods, that operate by optimizing a distribution of parameters, represent the best options for the evolution of robust neural network controllers.