It's Time to Revisit the Use of FPGAs for Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8444
- @InProceedings{Crary:2024:GPTP,
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author = "Christopher Crary and Greg Stitt and
Bogdan Burlacu and Wolfgang Banzhaf",
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title = "It's Time to Revisit the Use of {FPGAs} for Genetic
Programming",
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booktitle = "Genetic Programming Theory and Practice XXI",
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year = "2024",
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editor = "Stephan M. Winkler and Wolfgang Banzhaf and
Ting Hu and Alexander Lalejini",
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series = "Genetic and Evolutionary Computation",
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pages = "275--295",
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address = "University of Michigan, USA",
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month = jun # " 6-8",
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming, FPGA",
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isbn13 = "978-981-96-0076-2",
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DOI = "
doi:10.1007/978-981-96-0077-9_14",
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abstract = "In the past, field-programmable gate arrays (FPGAs)
have had some notable successes when employed for
Boolean and fixed-point genetic programming (GP)
systems, but the more common floating-point
representations were largely off limits, due to a
general lack of efficient device support. However,
recent work suggests that for both the training and
inference phases of floating-point-based GP,
contemporary FPGA technologies may enable significant
performance and energy improvements—potentially
multiple orders of magnitude when compared to
general-purpose CPU/GPU devices. we highlight the
potential advantages and challenges of using FPGAs for
GP systems, and we showcase how novel algorithmic
considerations likely need to be made in order to
extract the most benefits from specialized hardware.
Primarily, we consider tree-based GP, although we
include suggestions for other program representations.
Overall, we conclude that the GP community should
earnestly revisit the use of FPGA devices, especially
the tailoring of state-of-the-art algorithms to FPGAs,
since valuable enhancements may be realized. Most
notably, FPGAs may allow for faster and/or less costly
GP runs, in which case it may also be possible for
better solutions to be found when allowing an FPGA to
consume the same amount of runtime/energy as another
platform.",
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notes = "Published in 2025 after the workshop",
- }
Genetic Programming entries for
Christopher C Crary
Greg Stitt
Bogdan Burlacu
Wolfgang Banzhaf
Citations