Distributed Genetic Programming Models with Application to Logic Synthesis on FPGAs

Created by W.Langdon from gp-bibliography.bib Revision:1.3949

@PhdThesis{fernandez:thesis,
  author =       "Francisco {Fernandez de Vega}",
  title =        "Distributed Genetic Programming Models with
                 Application to Logic Synthesis on FPGAs",
  school =       "University of Extremadura",
  year =         "2001",
  email =        "fcofdez@unex.es",
  keywords =     "genetic algorithms, genetic programming,
                 reconfigurable hardware, EHW, PADGP, IMGP",
  broken =       "http://cum.unex.es/profes/profes/fcofdez/escritorio/investigacion/pgp/thesis/phd.html",
  URL =          "http://www.researchgate.net/publication/256474009_Distributed_Genetic_Programming_Models_with_Application_to_Logic_Synthesis_on_FPGAs._PhD._Thesis._2001",
  size =         "156 pages",
  notes =        "For Spanish version see
                 \cite{fernandez:thesis:espanol}

                 CONCLUSIONS AND FINAL REMARKS

                 We have presented a new implementation of GP - based on
                 MPI - which allows us to make use of parallelism as
                 well as experimenting with different communication
                 topologies and GP parameters

                 We have compared performances of this methodology
                 ?PADGP ? with classic GP. The tool was applied to the
                 study of two important parameters that affect
                 convergence results on PADGP: the number and size of
                 populations. By means of this study, we have observed
                 the existence of a region of effort which defines the
                 best number of individuals we must use when employing a
                 given number of populations with PADGP.

                 This region of effort has been detected both in
                 benchmark problems and in ?real life? problems.

                 We have also presented random topology as a way of
                 improving convergence when using PADGP.

                 We have used PADGP with random topology and compared it
                 to classic GP. This comparison showed that the former
                 gives better results.

                 We have also compared random topology and grid topology
                 and we have shown that results are similar.
                 Nevertheless random topology requires a smaller amount
                 of communication processes.

                 We have presented a methodology that is based on PADGP,
                 and which aids medical diagnosing. We used this problem
                 to check the validity of results obtained in the
                 benchmark problem, while we also proposed PADGP as an
                 appropriate methodology for extracting medical
                 knowledge.

                 We have studied isolated subpopulations (IMGP) as a
                 limit case of PADGP and we have experimentally seen
                 that IMGP obtains similar convergence results than GP;
                 sometimes results are even better if the total number
                 of individuals is high.

                 We have then dealt with an optimisation problem: the
                 problem of placement and routing on FPGAs. We have
                 developed a new methodology based on GP, and this
                 allows us to represent circuits by means of GP trees.
                 Furthermore, the methodology achieved the proposed
                 goal: finding several ways of placing and routing
                 circuits on reconfigurable hardware. The problem was
                 later used for checking the conclusions which had been
                 reached in the first part of this research. All
                 statistical results obtained are in agreement with
                 those obtained from benchmark problems.

                 We think that the main goals we established at the
                 beginning have been achieved: checking the usefulness
                 of PADGP with random communications and developing a
                 methodology for logic synthesis on FPGAs. In the
                 researching process we discovered the concept of region
                 of effort and we obtained interesting conclusions via
                 the use of IMGP.

                 Results we obtained during our research have been
                 published in the main conferences and reviews that deal
                 with the different topics addressed in this thesis (see
                 References).",
  notes =        "Gruau embryology. p113 Figure how recessive genes work
                 in crossover between trees. Isolated multi-population
                 genetic programming (IMGP)",
}

Genetic Programming entries for Francisco Fernandez de Vega

Citations