Accelerating GP Genome Evaluation Through Real Compilation with a Multiple Program Single Data Approach
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @InProceedings{de-melo:2024:GECCOcomp,
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author = "Vinicius Veloso {De Melo} and Wolfgang Banzhaf and
Giovanni Iacca",
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title = "Accelerating {GP} Genome Evaluation Through Real
Compilation with a Multiple Program Single Data
Approach",
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booktitle = "Symbolic Regression",
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year = "2024",
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editor = "William {La Cava} and Steven Gustafson",
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pages = "2041--2049",
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address = "Melbourne, Australia",
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series = "GECCO '24",
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month = "14-18 " # 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, multiple
program single data",
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isbn13 = "979-8-4007-0495-6",
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DOI = "doi:10.1145/3638530.3664168",
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size = "9 pages",
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abstract = "Genetic Programming (GP) presents a unique challenge
in fitness evaluation due to the need to repeatedly
execute the evolved programs, often represented as tree
structures, to assess their quality on multiple input
data. Traditional approaches rely on interpreting these
program trees, which can be computationally expensive.
This paper proposes an optimization method that
leverages code generation using a novel strategy and
Just-In-Time (JIT) compilation to significantly improve
the efficiency of fitness evaluation in GP. We propose
to revisit using an actual compiler to transform a GP
individual into native computer code executable quickly
on the CPU. Our GP implementation is a simple
tree-based approach that makes it easy for researchers
to experiment with, while the evaluation function shows
high performance. Preliminary results in Symbolic
Regression on artificial datasets demonstrate that our
approach is more than 6\texttimes{} faster than a
popular GP framework that uses high-performance
single-function evaluation but in a stack-based
interpreter mode.",
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notes = "GECCO-2024 SymReg A Recombination of the 33rd
International Conference on Genetic Algorithms (ICGA)
and the 29th Annual Genetic Programming Conference
(GP)",
- }
Genetic Programming entries for
Vinicius Veloso de Melo
Wolfgang Banzhaf
Giovanni Iacca
Citations