# Can Genetic Programming Do Manifold Learning Too?

arXiv: https://arxiv.org/abs/1902.02949 · HTML: https://mlsanity.com/paper/1902.02949/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Feb 2019.

## Built on by (most cited first, 5 of 5)

- [On genetic programming representations and fitness functions for interpretable dimensionality reduction](https://mlsanity.com/paper/2203.00528/) (2022)
- [Genetic Programming for Manifold Learning: Preserving Local Topology](https://mlsanity.com/paper/2108.09914/) (2021)
- [Improving Algorithm-Selection and Performance-Prediction via Learning Discriminating Training Samples](https://mlsanity.com/paper/2404.05359/) (2024)
- [Evolutionary Computation for the Design and Enrichment of General-Purpose Artificial Intelligence Systems: Survey and Prospects](https://mlsanity.com/paper/2407.08745/) (2024)
- [Genetic Programming for Explainable Manifold Learning](https://mlsanity.com/paper/2403.14139/) (2024)

## Abstract (arXiv)

Exploratory data analysis is a fundamental aspect of knowledge discovery that aims to find the main characteristics of a dataset. Dimensionality reduction, such as manifold learning, is often used to reduce the number of features in a dataset to a manageable level for human interpretation. Despite this, most manifold learning techniques do not explain anything about the original features nor the true characteristics of a dataset. In this paper, we propose a genetic programming approach to manifold learning called GP-MaL which evolves functional mappings from a high-dimensional space to a lower dimensional space through the use of interpretable trees. We show that GP-MaL is competitive with existing manifold learning algorithms, while producing models that can be interpreted and re-used on unseen data. A number of promising future directions of research are found in the process.