# Effective Mutation Rate Adaptation through Group Elite Selection

arXiv: https://arxiv.org/abs/2204.04817 · HTML: https://mlsanity.com/paper/2204.04817/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 45 references that are in the map.
- First posted on arXiv 11 Apr 2022.

## Builds on (most cited first, 8 of 8)

- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Evolution Strategies as a Scalable Alternative to Reinforcement Learning](https://mlsanity.com/paper/1703.03864/) (2017)
- [The CMA Evolution Strategy: A Tutorial](https://mlsanity.com/paper/1604.00772/) (2016)
- [Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning](https://mlsanity.com/paper/1712.06567/) (2017)
- [A Review on Bilevel Optimization: From Classical to Evolutionary Approaches and Applications](https://mlsanity.com/paper/1705.06270/) (2017)
- [Self-Adjusting Mutation Rates with Provably Optimal Success Rules](https://mlsanity.com/paper/1902.02588/) (2019)
- [Self-adaptation of Mutation Rates in Non-elitist Populations](https://mlsanity.com/paper/1606.05551/) (2016)

## Built on by (most cited first, 6 of 6)

- [evosax: JAX-based Evolution Strategies](https://mlsanity.com/paper/2212.04180/) (2022)
- [Discovering Attention-Based Genetic Algorithms via Meta-Black-Box Optimization](https://mlsanity.com/paper/2304.03995/) (2023)
- [NeuroEvoBench: Benchmarking Evolutionary Optimizers for Deep Learning Applications](https://mlsanity.com/paper/2311.02394/) (2023)
- [Lottery Tickets in Evolutionary Optimization: On Sparse Backpropagation-Free Trainability](https://mlsanity.com/paper/2306.00045/) (2023)
- [Hypernetworks That Evolve Themselves](https://mlsanity.com/paper/2512.16406/) (2025)
- [Learning Evolution via Optimization Knowledge Adaptation](https://mlsanity.com/paper/2501.02200/) (2025)

## Abstract (arXiv)

Evolutionary algorithms are sensitive to the mutation rate (MR); no single value of this parameter works well across domains. Self-adaptive MR approaches have been proposed but they tend to be brittle: Sometimes they decay the MR to zero, thus halting evolution. To make self-adaptive MR robust, this paper introduces the Group Elite Selection of Mutation Rates (GESMR) algorithm. GESMR co-evolves a population of solutions and a population of MRs, such that each MR is assigned to a group of solutions. The resulting best mutational change in the group, instead of average mutational change, is used for MR selection during evolution, thus avoiding the vanishing MR problem. With the same number of function evaluations and with almost no overhead, GESMR converges faster and to better solutions than previous approaches on a wide range of continuous test optimization problems. GESMR also scales well to high-dimensional neuroevolution for supervised image-classification tasks and for reinforcement learning control tasks. Remarkably, GESMR produces MRs that are optimal in the long-term, as demonstrated through a comprehensive look-ahead grid search. Thus, GESMR and its theoretical and empirical analysis demonstrate how self-adaptation can be harnessed to improve performance in several applications of evolutionary computation.