# Investigating the Parameter Space of Evolutionary Algorithms

arXiv: https://arxiv.org/abs/1706.04119 · HTML: https://mlsanity.com/paper/1706.04119/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jun 2017.

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

- [Expressive Mortality Models through Gaussian Process Kernels](https://mlsanity.com/paper/2305.01728/) (2023)

## Abstract (arXiv)

The practice of evolutionary algorithms involves the tuning of many parameters. How big should the population be? How many generations should the algorithm run? What is the (tournament selection) tournament size? What probabilities should one assign to crossover and mutation? Through an extensive series of experiments over multiple evolutionary algorithm implementations and problems we show that parameter space tends to be rife with viable parameters, at least for 25 the problems studied herein. We discuss the implications of this finding in practice.