- author = "Yazmin Maldonado and Ruben Salas and Joel A. Quevedo and Rogelio Valdez and Leonardo Trujillo",
- title = "{GSGP}-hardware: instantaneous symbolic regression with an {FPGA} implementation of geometric semantic genetic programming",
- journal = "Genetic Programming and Evolvable Machines",
- year = "2024",
- volume = "25",
- pages = "Article no 18",
- note = "Online first",
- keywords = "genetic algorithms, genetic programming, evolvable hardware, VHDL, FPGA, Geometric Semantic Genetic Programming, GSGP",
- ISSN = "1389-2576",
-
DOI = "
doi:10.1007/s10710-024-09491-5",
- abstract = "more prevalent with the continued development of the Internet-of-Things and the increased need for efficient and distributed learning on the Edge. GSGP-Hardware, a fully pipelined and parallel design of GSGP developed fully using VHDL, for implementation on FPGA devices. Using Vivado AMD-Xilinx for synthesis and simulation, GSGP-Hardware achieves an approximate improvement in efficiency, in terms of run time and GPops/s, of three and four orders of magnitude, respectively, compared with the state-of-the-art GPU implementation. This is a performance increase that has not been achieved by other FPGA-based implementations of genetic programming. This is possible due to the manner in which GSGP evolves a model, and competitive accuracy is achieved by incorporating simple but powerful enhancements to the original GSGP algorithm. GSGP-Hardware allows for instantaneous symbolic regression, opening up new application domains for this powerful variant of genetic programming.",
-
notes = "See MSc M1921052
https://rinacional.tecnm.mx/bitstream/TecNM/6652/1/Tesis%20Ruben%20Dario%20Salas%20Villegas.pdf
M20210005 https://rinacional.tecnm.mx/bitstream/TecNM/6647/1/Tesis%20Joel%20Antonio%20Quevedo%20Felix.pdf
Posgrado en Ciencias de la Ingenieria Tecnologico Nacional de Mexico/IT de Tijuana, Tijuana, Mexico",
