GSGP-Hardware: FPGA implementation of GSGP
Created by W.Langdon from
gp-bibliography.bib Revision:1.8519
- @InProceedings{maldonado:2025:GECCOcomp,
-
author = "Yazmin Maldonado and Ruben Salas and
Joel A. Quevedo and Rogelio Valdez and Leonardo Trujillo",
-
title = "{GSGP-Hardware:} {FPGA} implementation of {GSGP}",
-
booktitle = "Proceedings of the 2025 Genetic and Evolutionary
Computation Conference: Hot off the Press",
-
year = "2025",
-
editor = "Eric Medvet",
-
pages = "45--46",
-
address = "Malaga, Spain",
-
series = "GECCO '25 Companion",
-
month = "14-18 " # jul,
-
organisation = "SIGEVO",
-
publisher = "Association for Computing Machinery",
-
publisher_address = "New York, NY, USA",
-
keywords = "genetic algorithms, genetic programming, geometric
semantic genetic programming, VHDL, FPGA",
-
isbn13 = "979-8-4007-1464-1",
-
URL = "
https://doi.org/10.1145/3712255.3734246",
-
DOI = "
doi:10.1145/3712255.3734246",
-
size = "2 pages",
-
abstract = "Geometric Semantic Genetic Programming (GSGP) enhances
traditional GP by using search operators that act on
the syntax of solutions with bounded effects on
semantics, transforming symbolic regression into a
uni-modal search problem. CPU and GPU implementations
of GSGP have demonstrated efficiency by eliminating the
need to evaluate variable-length syntactic models, but
they remain unsuitable for real-time or
resource-constrained scenarios. This paper presents
GSGP-Hardware, a fully pipelined and parallel FPGA
implementation developed using VHDL. Our solution
features a custom fixed-point arithmetic division
algorithm, concurrent individual evaluation, and
optimal mutation step computation. Validated on five
real-world benchmarks and synthesized using Vivado
AMD-Xilinx, GSGP-Hardware achieves competitive
predictive accuracy while delivering unprecedented
performance gains; three orders of magnitude in runtime
and four orders of magnitude in GPops/s compared to
state-of-the-art GPU implementations. This work enables
instantaneous symbolic regression with significantly
lower power consumption, opening new possibilities for
evolutionary algorithms in domains where computational
resources and energy efficiency are limited.",
-
notes = "GECCO-2025 A Recombination of the 34th International
Conference on Genetic Algorithms (ICGA) and the 30th
Annual Genetic Programming Conference (GP)",
- }
Genetic Programming entries for
Yazmin Maldonado Robles
Ruben Dario Salas Villegas
Joel Antonio Quevedo Felix
Rogelio Valdez
Leonardo Trujillo
Citations