Probabilistic Lexicase Selection
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{ding:2023:GECCO,
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author = "Li Ding and Edward Pantridge and Lee Spector",
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title = "Probabilistic Lexicase Selection",
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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 = "1073--1081",
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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, symbolic
regression, machine learning, evolutionary algorithms,
program synthesis, parent selection",
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isbn13 = "9798400701191",
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DOI = "doi:10.1145/3583131.3590375",
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size = "9 pages",
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abstract = "Lexicase selection is a widely used parent selection
algorithm in genetic programming, known for its success
in various task domains such as program synthesis,
symbolic regression, and machine learning. Due to its
non-parametric and recursive nature, calculating the
probability of each individual being selected by
lexicase selection has been proven to be an NP-hard
problem, which discourages deeper theoretical
understanding and practical improvements to the
algorithm. In this work, we introduce probabilistic
lexicase selection (plexicase selection), a novel
parent selection algorithm that efficiently
approximates the probability distribution of lexicase
selection. Our method not only demonstrates superior
problem-solving capabilities as a semantic-aware
selection method, but also benefits from having a
probabilistic representation of the selection process
for enhanced efficiency and flexibility. Experiments
are conducted in two prevalent domains in genetic
programming: program synthesis and symbolic regression,
using standard benchmarks including PSB and SRBench.
The empirical results show that plexicase selection
achieves state-of-the-art problem-solving performance
that is competitive to the lexicase selection, and
significantly outperforms lexicase selection in
computation efficiency.",
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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
Li Ding
Edward R Pantridge
Lee Spector
Citations