Using MOEA to evolve a combinational circuit on a FPGA chip

Created by W.Langdon from gp-bibliography.bib Revision:1.4208

  author =       "Changhao Piao and Jin Wang and Zhiyong Luo",
  title =        "Using MOEA to evolve a combinational circuit on a FPGA
  booktitle =    "7th World Congress on Intelligent Control and
                 Automation, WCICA 2008",
  year =         "2008",
  month =        jun,
  pages =        "6267--6271",
  keywords =     "genetic algorithms, genetic programming, Cartesian
                 genetic programming, FPGA chip, MOEA, combinational
                 circuit, evolutionary circuit design, multiobjective
                 evolutionary algorithm, reconfigurable circuit
                 architecture, combinational circuits, field
                 programmable gate arrays",
  DOI =          "doi:10.1109/WCICA.2008.4593873",
  abstract =     "A combinational circuit design method that can be
                 completely implemented on a FPGA is presented. For
                 carrying out faster evolution in evolutionary circuit
                 design, a sub population based MOEA (multiobjective
                 evolutionary algorithm) is employed in which the
                 reconfigurable circuit (RC) architecture is encoded by
                 Cartesian Genetic Programming (CGP). For hardware
                 implementation, the Celoxica RC1000 PCI is employed
                 which includes Xilinx Virtex xcv 2000E FPGA chip. This
                 PCI card is communicating with host PC and acting as an
                 evolvable platform. MOEA adopted modules are designed
                 into a FPGA chip for discussing the rationality of
                 circuit design method. Results of direct evolution and
                 results of incremental evolution is compared, it shows
                 MOEA is most efficient in the aspect of speeding up the
                 convergence of evolution.",
  notes =        "Also known as \cite{4593873}",

Genetic Programming entries for Changhao Piao Jin Wang Zhiyong Luo