Using Bayesian Optimization to Improve Hyperparameter Search in TPOT

Created by W.Langdon from gp-bibliography.bib Revision:1.8120

@InProceedings{kenny:2024:GECCO,

• author = "Angus Kenny and Tapabrata Ray and Steffen Limmer and Hemant Kumar Singh and Tobias Rodemann and Markus Olhofer",
• title = "Using Bayesian Optimization to Improve Hyperparameter Search in {TPOT}",
• booktitle = "Proceedings of the 2024 Genetic and Evolutionary Computation Conference",
• year = "2024",
• editor = "Jean-Baptiste Mouret and Kai Qin and Julia Handl and Xiaodong Li and Markus Wagner and Mario Garza-Fabre and Kate Smith-Miles and Richard Allmendinger and Ying Bi and Grant Dick and Amir H Gandomi and Marcella Scoczynski Ribeiro Martins and Hirad Assimi and Nadarajen Veerapen and Yuan Sun and Mario Andres Munyoz and Ahmed Kheiri and Nguyen Su and Dhananjay Thiruvady and Andy Song and Frank Neumann and Carla Silva",
• pages = "340--348",
• address = "Melbourne, Australia",
• series = "GECCO '24",
• month = "14-18 " # jul,
• organisation = "SIGEVO",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, Evolutionary Machine Learning",
• isbn13 = "979-8-4007-0494-9",
• DOI = "doi:10.1145/3638529.3654061",
• size = "9 pages",
• abstract = "Automated machine learning (AutoML) has emerged as a pivotal tool for applying machine learning (ML) models to real-world problems. Tree-based pipeline optimization tool (TPOT) is an AutoML framework known for effectively solving complex tasks. TPOT's search involves two fundamental objectives: finding optimal pipeline structures (i.e., combinations of ML operators) and identifying suitable hyperparameters for these structures. While its use of genetic programming enables TPOT to excel in structural search, its hyperparameter search, involving discretization and random selection from extensive potential value ranges, can be computationally inefficient. This paper presents a novel methodology that heavily restricts the initial hyperparameter search space, directing TPOT's focus towards structural exploration. As the search evolves, Bayesian optimization (BO) is used to refine the hyperparameter space based on data from previous pipeline evaluations. This method leads to a more targeted search, crucial in situations with limited computational resources. Two variants of this approach are proposed and compared with standard TPOT across six datasets, with up to 20 features and 20,000 samples. The results show the proposed method is competitive with canonical TPOT, and outperforms it in some cases. The study also provides new insights into the dynamics of pipeline structure and hyperparameter search within TPOT.",
• notes = "GECCO-2024 EML A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for Angus Kenny Tapabrata Ray Steffen Limmer Hemant Kumar Singh Tobias Rodemann Markus Olhofer

Citations