Genetic programming and its applications to the synthesis of digital logic

Created by W.Langdon from gp-bibliography.bib Revision:1.4394

  author =       "Karen M. Dill and James H. Herzog and 
                 Marek A. Perkowski",
  title =        "Genetic programming and its applications to the
                 synthesis of digital logic",
  booktitle =    "IEEE Pacific Rim Conference on Communications,
                 Computers and Signal Processing, PACRIM 1997",
  year =         "1997",
  volume =       "2",
  pages =        "823--826",
  address =      "Victoria, BC, Canada",
  month =        "20-22 " # aug,
  note =         "Networking the Pacific Rim, 10 Years PACRIM
  keywords =     "genetic algorithms, genetic programming, EHW, logic
                 circuits, logic CAD, digital logic synthesis, arbitrary
                 logic expressions, logic synthesis, problem
                 applicability, optimization criterion, logic gates,
                 population sizes, complete function coverage,
                 experimental test results, randomly designed functions,
                 input variables, logic equations, function coverage,
                 training set size, small training sets, function
  ISBN =         "0-7803-3905-3",
  DOI =          "doi:10.1109/PACRIM.1997.620386",
  size =         "4 pages",
  abstract =     "Genetic programming is applied to the synthesis of
                 arbitrary logic expressions. As a new method of logic
                 synthesis, this technique is uniquely advantageous in
                 its flexibility for both problem applicability and
                 optimisation criterion. A number of experiments were
                 conducted exploring this method with different types of
                 logic gates and population sizes. While complete
                 function coverage is not guaranteed, the best
                 experimental test results over eight randomly designed
                 functions, of four to seven input variables, have
                 produced logic equations with a 98.4percent function
                 coverage. In addition, the relation between the
                 training set size for the genetic program and function
                 coverage was also empirically explored. These
                 experiments showed that only small training sets were
                 necessary for function recognition.",

Genetic Programming entries for Karen M Dill James H Herzog Marek A Perkowski