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,

• author = "Christopher Crary and Greg Stitt and Bogdan Burlacu and Wolfgang Banzhaf",
• title = "It's Time to Revisit the Use of {FPGAs} for Genetic Programming",
• booktitle = "Genetic Programming Theory and Practice XXI",
• year = "2024",
• editor = "Stephan M. Winkler and Wolfgang Banzhaf and Ting Hu and Alexander Lalejini",
• series = "Genetic and Evolutionary Computation",
• pages = "275--295",
• address = "University of Michigan, USA",
• month = jun # " 6-8",
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming, FPGA",
• isbn13 = "978-981-96-0076-2",
• DOI = "doi:10.1007/978-981-96-0077-9_14",
• 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.",
• notes = "Published in 2025 after the workshop",

}

Genetic Programming entries for Christopher C Crary Greg Stitt Bogdan Burlacu Wolfgang Banzhaf

Citations