# Optimal Parameter Settings for the (1+(λ, λ)) Genetic Algorithm

arXiv: https://arxiv.org/abs/1604.01088 · HTML: https://mlsanity.com/paper/1604.01088/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Apr 2016.

## Built on by (most cited first, 1 of 1)

- [Lazy Parameter Tuning and Control: Choosing All Parameters Randomly From a Power-Law Distribution](https://mlsanity.com/paper/2104.06714/) (2021)

## Abstract (arXiv)

The (1+(λ,λ)) genetic algorithm is one of the few algorithms for which a super-constant speed-up through the use of crossover could be proven. So far, this algorithm has been used with parameters based also on intuitive considerations. In this work, we rigorously regard the whole parameter space and show that the asymptotic time complexity proven by Doerr and Doerr (GECCO 2015) for the intuitive choice is best possible among all settings for population size, mutation probability, and crossover bias.