# An Adaptive and Near Parameter-free Evolutionary Computation Approach Towards True Automation in AutoML

arXiv: https://arxiv.org/abs/2001.10178 · HTML: https://mlsanity.com/paper/2001.10178/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jan 2020.

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

- [Automated Evolutionary Approach for the Design of Composite Machine Learning Pipelines](https://mlsanity.com/paper/2106.15397/) (2021)
- [Task Selection for AutoML System Evaluation](https://mlsanity.com/paper/2208.12754/) (2022)
- [Multi-Objective Evolutionary Design of Composite Data-Driven Models](https://mlsanity.com/paper/2103.01301/) (2021)
- [Integrating Hyperparameter Search into Model-Free AutoML with Context-Free Grammars](https://mlsanity.com/paper/2404.03419/) (2024)

## Abstract (arXiv)

A common claim of evolutionary computation methods is that they can achieve good results without the need for human intervention. However, one criticism of this is that there are still hyperparameters which must be tuned in order to achieve good performance. In this work, we propose a near "parameter-free" genetic programming approach, which adapts the hyperparameter values throughout evolution without ever needing to be specified manually. We apply this to the area of automated machine learning (by extending TPOT), to produce pipelines which can effectively be claimed to be free from human input, and show that the results are competitive with existing state-of-the-art which use hand-selected hyperparameter values. Pipelines begin with a randomly chosen estimator and evolve to competitive pipelines automatically. This work moves towards a truly automatic approach to AutoML.