Genens: An AutoML System for Ensemble Optimization Based on Developmental Genetic Programming

0537567 - ÚI 2021 RIV US eng C - Conference Paper (international conference)
Suchopárová, Gabriela - Neruda, Roman
Genens: An AutoML System for Ensemble Optimization Based on Developmental Genetic Programming.
2020 IEEE Symposium Series on Computational Intelligence (SSCI). New York: IEEE, 2020, s. 631-638. ISBN 978-1-7281-2547-3.
[IEEE SSCI 2020: IEEE Symposium Series on Computational Intelligence. Canberra / Online (AU), 01.12.2020-04.12.2020]
R&D Projects: GA ČR(CZ) GA18-23827S
Institutional support: RVO:67985807
Keywords : Machine learning * AutoML * Genetic programming * Developmental methods * Pipelines * Vegetation * Optimization * Task analysis * Machine learning algorithms * Computational modeling * Benchmark testing
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

We propose an AutoML system for pipeline optimization based on developmental genetic programming — genens. It is built atop of scikit-learn pipelines, and it focuses on both hyperparameter and architecture optimization. Compared to existing systems, it enables to optimize more complex ensembles, while exploring simpler models at the same time. The system has been evaluated on selected benchmark datasets from the AutoML benchmark, producing competitive results.
Permanent Link: http://hdl.handle.net/11104/0315396