Genetic Programming in Hardware

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Peter N. Martin",
  title =        "Genetic Programming in Hardware",
  school =       "University of Essex",
  year =         "2003",
  address =      "University of Essex, Wivenhoe Park, Colchester, UK",
  month =        mar,
  email =        "Pete Martin ",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "",
  URL =          "",
  size =         "214 pages",
  abstract =     "Genetic Programming in Hardware This thesis describes
                 a hardware implementation of a complete Genetic
                 Programming (GP) system using a Field Programmable Gate
                 Array, which is shown to speed-up GP by over 400 times
                 when compared with a software implementation of the
                 same algorithm. The hardware implements the creation of
                 the initial population, breeding operators, parallel
                 fitness evaluations and the output of the final

                 The research was motivated by the observation that GP
                 is usually implemented in software and run on general
                 purpose computers. Although software implementations
                 are flexible and easy to modify, they limit the
                 performance of GP thus restricting the range of
                 problems that GP can solve.

                 The hypothesis is that implementing GP in hardware
                 would speed up GP, allowing it to tackle problems which
                 are currently too hard for software based GP.

                 FPGAs are usually programmed using specialised hardware
                 design languages. An alternative approach is used in
                 this work that uses a high level language to hardware
                 compilation system, called Handel-C.

                 As part of this research, a number of general GP issues
                 are also explored. The parameters of GP are described
                 and arranged into a taxonomy of GP attributes. The
                 taxonomy allows GP problems to be categorised with
                 respect to their problem and GP specific attributes.
                 The role that the GP algorithm plays in problem solving
                 is shown to be part of a larger process called Meta-GP,
                 which describes the overall process of developing a GP
                 system and evolving a viable set of parameters to allow
                 GP to solve a problem. Three crossover operators are
                 investigated and a new operator, called single child
                 limiting crossover, is presented. This operator appears
                 to limit the tendency of GP to suffer from bloat. The
                 economics of implementing GP in hardware are analysed
                 and the costs and benefits are quantified. The thesis
                 concludes by suggesting some applications for hardware
  notes =        "",

Genetic Programming entries for Peter Martin