GPAM: Genetic Programming with Associative Memory
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Juza:2023:EuroGP,
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author = "Tadeas Juza and Lukas Sekanina",
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title = "{GPAM}: Genetic Programming with Associative Memory",
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booktitle = "EuroGP 2023: Proceedings of the 26th European
Conference on Genetic Programming",
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year = "2023",
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month = "12-14 " # apr,
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editor = "Gisele Pappa and Mario Giacobini and Zdenek Vasicek",
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series = "LNCS",
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volume = "13986",
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publisher = "Springer Verlag",
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address = "Brno, Czech Republic",
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pages = "68--83",
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organisation = "EvoStar, Species",
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keywords = "genetic algorithms, genetic programming, Associative
memory, Neural network, ANN, Weight compression,
Symbolic regression",
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isbn13 = "978-3-031-29572-0",
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URL = "https://www.fit.vut.cz/research/publication/12860",
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URL = "https://rdcu.be/c8UPG",
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DOI = "doi:10.1007/978-3-031-29573-7_5",
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size = "16 pages",
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abstract = "We focus on the evolutionary design of programs
capable of capturing more randomness and outliers in
the input data set than the standard genetic
programming (GP)-based methods typically allow. We
propose Genetic Programming with Associative Memory
(GPAM), a GP-based system for symbolic regression which
can use a small associative memory to store various
data points to better approximate the original data
set. The method is evaluated on five standard
benchmarks in which a certain number of data points is
replaced by randomly generated values. In another case
study, GPAM is used as an on-chip generator capable of
approximating the weights for a convolutional neural
network (CNN) to reduce the access to an external
weight memory. Using Cartesian genetic programming
(CGP), we evolved expression-memory pairs that can
generate weights of a single CNN layer. If the
associative memory contains 10percent of the original
weights, the weight generator evolved for a
convolutional layer can approximate the original
weights such that the CNN using the generated weights
shows less than a 1percent drop in the classification
accuracy on the MNIST data set.",
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notes = "Also known as \cite{FITPUB12860} Part of
\cite{Pappa:2023:GP} EuroGP'2023 held in conjunction
with EvoCOP2023, EvoMusArt2023 and
EvoApplications2023",
- }
Genetic Programming entries for
Tadeas Juza
Lukas Sekanina
Citations