# Autostacker: A Compositional Evolutionary Learning System

arXiv: https://arxiv.org/abs/1803.00684 · HTML: https://mlsanity.com/paper/1803.00684/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Mar 2018.

## Built on by (most cited first, 18 of 18)

- [VolcanoML: Speeding up End-to-End AutoML via Scalable Search Space Decomposition](https://mlsanity.com/paper/2107.08861/) (2021)
- [AlphaD3M: Machine Learning Pipeline Synthesis](https://mlsanity.com/paper/2111.02508/) (2021)
- [Assembled-OpenML: Creating Efficient Benchmarks for Ensembles in AutoML with OpenML](https://mlsanity.com/paper/2307.00285/) (2023)
- [Naive Automated Machine Learning](https://mlsanity.com/paper/2111.14514/) (2021)
- [XAutoML: A Visual Analytics Tool for Understanding and Validating Automated Machine Learning](https://mlsanity.com/paper/2202.11954/) (2022)
- [Privileged Zero-Shot AutoML](https://mlsanity.com/paper/2106.13743/) (2021)
- [PSEO: Optimizing Post-hoc Stacking Ensemble Through Hyperparameter Tuning](https://mlsanity.com/paper/2508.05144/) (2025)
- [AutoDES: AutoML Pipeline Generation of Classification with Dynamic Ensemble Strategy Selection](https://mlsanity.com/paper/2201.00207/) (2022)
- [Review of the state of the art in autonomous artificial intelligence](https://mlsanity.com/paper/2210.10659/) (2022)
- [Multi-level Stress Assessment from ECG in a Virtual Reality Environment using Multimodal Fusion](https://mlsanity.com/paper/2107.04566/) (2021)
- [Muddling Labels for Regularization, a novel approach to generalization](https://mlsanity.com/paper/2102.08769/) (2021)
- [Naive Automated Machine Learning -- A Late Baseline for AutoML](https://mlsanity.com/paper/2103.10496/) (2021)
- [A Deep Neural Networks ensemble workflow from hyperparameter search to inference leveraging GPU clusters](https://mlsanity.com/paper/2208.14046/) (2022)
- [Efficient End-to-End AutoML via Scalable Search Space Decomposition](https://mlsanity.com/paper/2206.09423/) (2022)
- [Clustering Indices based Automatic Classification Model Selection](https://mlsanity.com/paper/2305.13926/) (2023)
- [Grammar-based evolutionary approach for automated workflow composition with domain-specific operators and ensemble diversity](https://mlsanity.com/paper/2402.02124/) (2024)
- [Matching Problems to Solutions: An Explainable Way of Solving Machine Learning Problems](https://mlsanity.com/paper/2406.15662/) (2024)
- [Public Machine Learning Solver Framework for Novices in the Machine Learning Domain](https://mlsanity.com/paper/2606.08212/) (2026)

## Abstract (arXiv)

We introduce an automatic machine learning (AutoML) modeling architecture called Autostacker, which combines an innovative hierarchical stacking architecture and an Evolutionary Algorithm (EA) to perform efficient parameter search. Neither prior domain knowledge about the data nor feature preprocessing is needed. Using EA, Autostacker quickly evolves candidate pipelines with high predictive accuracy. These pipelines can be used as is or as a starting point for human experts to build on. Autostacker finds innovative combinations and structures of machine learning models, rather than selecting a single model and optimizing its hyperparameters. Compared with other AutoML systems on fifteen datasets, Autostacker achieves state-of-art or competitive performance both in terms of test accuracy and time cost.