# Self-Adjusting Mutation Rates with Provably Optimal Success Rules

## TL;DR

This paper analyzes how the (1+1) Evolutionary Algorithm's performance on LeadingOnes depends on success rule hyper-parameters, identifying optimal settings that match the best fitness-dependent mutation rates.

## Contribution

It provides a rigorous analysis of success-based parameter updates, establishing optimal hyper-parameter settings for the (1+1) EA on LeadingOnes.

## Key findings

- Optimal success rate is approximately 1/e.
- Small update strength F=1+o(1) yields best performance.
- Results extend to resampling variants of the (1+1) EA.

## Abstract

The one-fifth success rule is one of the best-known and most widely accepted techniques to control the parameters of evolutionary algorithms. While it is often applied in the literal sense, a common interpretation sees the one-fifth success rule as a family of success-based updated rules that are determined by an update strength $F$ and a success rate. We analyze in this work how the performance of the (1+1) Evolutionary Algorithm on LeadingOnes depends on these two hyper-parameters. Our main result shows that the best performance is obtained for small update strengths $F=1+o(1)$ and success rate $1/e$. We also prove that the running time obtained by this parameter setting is, apart from lower order terms, the same that is achieved with the best fitness-dependent mutation rate. We show similar results for the resampling variant of the (1+1) Evolutionary Algorithm, which enforces to flip at least one bit per iteration.