Parametrizing GP Trees for Better Symbolic Regression Performance through Gradient Descent

Created by W.Langdon from gp-bibliography.bib Revision:1.7662

@InProceedings{pietropolli:2023:GECCOcomp,

• author = "Gloria Pietropolli and Federico Julian {Camerota Verdu} and Luca Manzoni and Mauro Castelli",
• title = "Parametrizing {GP} Trees for Better Symbolic Regression Performance through Gradient Descent",
• 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 = "619--622",
• 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, local search, adam, gradient descent, memetic search: Poster",
• isbn13 = "9798400701191",
• DOI = "doi:10.1145/3583133.3590574",
• size = "4 pages",
• abstract = "Symbolic regression is a common problem in genetic programming (GP), but the syntactic search carried out by the standard GP algorithm often struggles to tune the learned expressions. On the other hand, gradient-based optimizers can efficiently tune parametric functions by exploring the search space locally. While there is a large amount of research on the combination of evolutionary algorithms and local search (LS) strategies, few of these studies deal with GP. To get the best from both worlds, we propose embedding learnable parameters in GP programs and combining the standard GP evolutionary approach with a gradient-based refinement of the individuals employing the Adam optimizer. We devise two different algorithms that differ in how these parameters are shared in the expression operators and report experimental results performed on a set of standard real-life application datasets. Our findings show that the proposed gradient-based LS approach can be effectively combined with GP to outperform the original algorithm.",
• 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 Gloria Pietropolli Federico Julian Camerota Verdu Luca Manzoni Mauro Castelli

Citations