Symbolic Regression Trees as Embedded Representations
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{caetano:2023:GECCO,
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author = "Victor Caetano and Matheus {Candido Teixeira} and
Gisele Lobo Pappa",
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title = "Symbolic Regression Trees as Embedded
Representations",
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booktitle = "Proceedings of the 2023 Genetic and Evolutionary
Computation Conference",
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year = "2023",
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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",
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pages = "411--419",
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address = "Lisbon, Portugal",
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series = "GECCO '23",
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month = "15-19 " # jul,
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organisation = "SIGEVO",
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publisher = "Association for Computing Machinery",
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publisher_address = "New York, NY, USA",
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keywords = "genetic algorithms, genetic programming, embedded
representations, semantics, transformers",
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isbn13 = "9798400701191",
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DOI = "doi:10.1145/3583131.3590423",
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size = "9 pages",
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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.",
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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
VĂctor de Souza Caetano
Matheus Candido Teixeira
Gisele L Pappa
Citations