Hybridizing TPOT with Bayesian Optimization

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{kenny:2023:GECCO,

• author = "Angus Kenny and Tapabrata Ray and Steffen Limmer and Hemant Kumar Singh and Tobias Rodemann and Markus Olhofer",
• title = "Hybridizing {TPOT} with Bayesian Optimization",
• booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference",
• year = "2023",
• editor = "Sara Silva and Luis Paquete and Leonardo Vanneschi and Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and Arnaud Liefooghe and Bing Xue and Ying Bi and Nelishia Pillay and Irene Moser and Arthur Guijt and Jessica Catarino and Pablo Garcia-Sanchez and Leonardo Trujillo and Carla Silva and Nadarajen Veerapen",
• pages = "502--510",
• address = "Lisbon, Portugal",
• series = "GECCO '23",
• month = "15-19 " # jul,
• organisation = "SIGEVO",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "9798400701191",
• DOI = "doi:10.1145/3583131.3590364",
• size = "9 pages",
• abstract = "Tree-based pipeline optimization tool (TPOT) is used to automatically construct and optimize machine learning pipelines for classification or regression tasks. The pipelines are represented as trees comprising multiple data transformation and machine learning operators --- each using discrete hyper-parameter spaces --- and optimized with genetic programming. During the evolution process, TPOT evaluates numerous pipelines which can be challenging when computing budget is limited. In this study, we integrate TPOT with Bayesian Optimization (BO) to extend its ability to search across continuous hyper-parameter spaces, and attempt to improve its performance when there is a limited computational budget. Multiple hybrid variants are proposed and systematically evaluated, including (a) sequential/periodic use of BO and (b) use of discrete/continuous search spaces for BO. The performance of these variants is assessed using 6 data sets with up to 20 features and 20,000 samples. Furthermore, an adaptive variant was designed where the choice of whether to apply TPOT or BO is made automatically in each generation. While the variants did not produce results that are significantly better than {"}standard{"} TPOT, the study uncovered important insights into the behavior and limitations of TPOT itself which is valuable in designing improved variants.",
• notes = "GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)",

}

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

Citations