# Self-adaptation of Mutation Rates in Non-elitist Populations

arXiv: https://arxiv.org/abs/1606.05551 · HTML: https://mlsanity.com/paper/1606.05551/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jun 2016.

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

- [Adversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-Art](https://mlsanity.com/paper/2112.12310/) (2021)
- [Effective Mutation Rate Adaptation through Group Elite Selection](https://mlsanity.com/paper/2204.04817/) (2022)
- [Fairness in generative modeling](https://mlsanity.com/paper/2210.03517/) (2022)
- [Evolutionary Retrofitting](https://mlsanity.com/paper/2410.11330/) (2024)
- [Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training](https://mlsanity.com/paper/2507.01752/) (2025)
- [Selection Hyper-heuristics Can Automatically Adjust the Learning Period to Optimally Solve Pseudo-Boolean Problems](https://mlsanity.com/paper/2605.29916/) (2026)

## Abstract (arXiv)

The runtime of evolutionary algorithms (EAs) depends critically on their parameter settings, which are often problem-specific. Automated schemes for parameter tuning have been developed to alleviate the high costs of manual parameter tuning. Experimental results indicate that self-adaptation, where parameter settings are encoded in the genomes of individuals, can be effective in continuous optimisation. However, results in discrete optimisation have been less conclusive. Furthermore, a rigorous runtime analysis that explains how self-adaptation can lead to asymptotic speedups has been missing. This paper provides the first such analysis for discrete, population-based EAs. We apply level-based analysis to show how a self-adaptive EA is capable of fine-tuning its mutation rate, leading to exponential speedups over EAs using fixed mutation rates.