Symbolic Regression Trees as Embedded Representations

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

@InProceedings{caetano:2023:GECCO,

• author = "Victor Caetano and Matheus Candido Teixeira and Gisele Lobo Pappa",
• title = "Symbolic Regression Trees as Embedded Representations",
• 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 = "411--419",
• 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, embedded representations, semantics, transformers",
• isbn13 = "9798400701191",
• DOI = "doi:10.1145/3583131.3590423",
• size = "9 pages",
• abstract = "Representation learning is an area responsible for learning data representations that makes it easier for machine learning algorithms to extract useful information from them. Deep learning currently has the most effective methods for this task and can learn distributed representations - also known as embeddings - able to represent different properties of the data and their relationship. In this direction, this paper introduces a new way to look at tree-like GP individuals for symbolic regression. Given a set of predefined operators and a sufficiently large number of solutions sampled from the space, we train a transformer to learn an encoding/decoding function. By transforming a tree representation into a distributed representation, we are able to measure distances between trees in a much more efficient way and, more importantly, generate the potential for these representations to capture semantics. We show the distance accounting for embedding presents results very similar to those of a tree-edition, which reflects their syntactic similarity. Although the model as it stands is not able to capture semantics yet, we show its potential by using the generated tree-representation model in a simple task: measuring distances between trees in a fitness-sharing scenario.",
• 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 Victor Caetano Matheus Candido Teixeira Gisele L Pappa

Citations