Genetic Algorithms for AutoML

Learn how genetic algorithms automate feature preprocessing, selection, and hyperparameter tuning using sklearn-genetic-opt to build efficient, high‑performing AutoML pipelines in real time.

Overview

AutoML is a set of strategies to optimize the process of building machine learning models, common patterns include feature preprocessing, selection, and hyperparameter tuning. Explore the ideas and code behind metaheuristics to build your AutoML pipeline using sklearn-genetic-opt, an open-source library.

Links

https://github.com/rodrigo-arenas/Sklearn-genetic-opt
Sklearn-genetic-opt optimizes ML hyperparameters and features using evolutionary algorithms.
https://sklearn-genetic-opt.readthedocs.io/en/stable/
`sklearn-genetic-opt` tunes scikit-learn hyperparameters and features using DEAP evolutionary algorithms.

Tech stack