# Evolving Evolutionary Algorithms using Multi Expression Programming

arXiv: https://arxiv.org/abs/2109.13737 · HTML: https://mlsanity.com/paper/2109.13737/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 11 references that are in the map.
- First posted on arXiv 22 Aug 2021.

## Built on by (most cited first, 4 of 4)

- [Multi Expression Programming -- an in-depth description](https://mlsanity.com/paper/2110.00367/) (2021)
- [New Evolutionary Computation Models and their Applications to Machine Learning](https://mlsanity.com/paper/2110.00468/) (2021)
- [Evolving Evolutionary Algorithms with Patterns](https://mlsanity.com/paper/2110.05951/) (2021)
- [Improving the Search by Encoding Multiple Solutions in a Chromosome](https://mlsanity.com/paper/2110.11239/) (2021)

## Abstract (arXiv)

Finding the optimal parameter setting (i.e. the optimal population size, the optimal mutation probability, the optimal evolutionary model etc) for an Evolutionary Algorithm (EA) is a difficult task. Instead of evolving only the parameters of the algorithm we will evolve an entire EA capable of solving a particular problem. For this purpose the Multi Expression Programming (MEP) technique is used. Each MEP chromosome will encode multiple EAs. An nongenerational EA for function optimization is evolved in this paper. Numerical experiments show the effectiveness of this approach.