Towards the development of a complete GP system on an FPGA using geometric semantic operators

Created by W.Langdon from gp-bibliography.bib Revision:1.4496

  author =       "Carlos Goribar-Jimenez and Yazmin Maldonado and 
                 Leonardo Trujillo and Mauro Castelli and 
                 Ivo Goncalves and Leonardo Vanneschi",
  booktitle =    "2017 IEEE Congress on Evolutionary Computation (CEC)",
  title =        "Towards the development of a complete GP system on an
                 FPGA using geometric semantic operators",
  year =         "2017",
  editor =       "Jose A. Lozano",
  pages =        "1932--1939",
  address =      "Donostia, San Sebastian, Spain",
  publisher =    "IEEE",
  isbn13 =       "978-1-5090-4601-0",
  abstract =     "Genetic Programming (GP) has been around for over two
                 decades and has been used in a wide range of practical
                 applications producing human competitive results in
                 several domains. In this paper we present a discussion
                 and a proposal of a GP algorithm that could be
                 conveniently implemented on an embedded system, as part
                 of a broader research project that pursues the
                 implementation of a complete GP system in a Field
                 Programmable Gate Array (FPGA). Motivated by the
                 significant time savings associated with such a
                 platform, as well as low power consumption, low
                 maintenance requirements, small size of the system and
                 the possibility of performing several parallel
                 processes. The proposal is focused on the Geometric
                 Semantic Genetic Programming (GSGP) approach that has
                 been recently introduced with promising results. GSGP
                 induces a unimodal fitness landscape, simplifying the
                 search process. The experimental work considers five
                 variants of GSGP, that incorporate local search
                 strategies, optimal mutations and alignment in error
                 space. Best results were obtained by a simple variant
                 that uses both the optimal mutation step and the
                 standard geometric semantic mutation, using three
                 difficult real-world problems to evaluate the methods,
                 outperforming the original GSGP formulation in terms of
                 fitness and empirical convergence.",
  keywords =     "genetic algorithms, genetic programming, convergence,
                 embedded systems, field programmable gate arrays,
                 geometry, parallel processing, search problems, FPGA,
                 GP algorithm, GP system development, GSGP, embedded
                 system, empirical convergence, error space alignment,
                 field programmable gate array, geometric semantic
                 genetic programming, geometric semantic operators,
                 local search strategies, maintenance requirements,
                 optimal mutation step, parallel processes, power
                 consumption, standard geometric semantic mutation, time
                 savings, unimodal fitness landscape, Arrays, GSM,
                 Proposals, Semantics, Standards",
  isbn13 =       "978-1-5090-4601-0",
  DOI =          "doi:10.1109/CEC.2017.7969537",
  month =        "5-8 " # jun,
  notes =        "IEEE Catalog Number: CFP17ICE-ART Also known as

Genetic Programming entries for Carlos Antonio Goribar Jimenez Yazmin Maldonado Robles Leonardo Trujillo Mauro Castelli Ivo Goncalves Leonardo Vanneschi