GenCo: A project report

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{oai:CiteSeerPSU:510392,
  author =       "Penousal Machado and Andre Dias and Amilcar Cardoso",
  title =        "{GenCo}: A project report",
  booktitle =    "ISAS 2001 -- International Symposium on Adaptive
                 Systems -- Evolutionary Computation and Probabilistic
                 Graphical Models",
  year =         "2002",
  editor =       "Alberto Ochoa Rodriguez",
  address =      "Havana, Cuba",
  month =        "19-23 " # mar,
  email =        "machado@dei.uc.pt, adias@student.dei.uc.pt,
                 amilcar@dei.uc.pt",
  keywords =     "genetic algorithms, genetic programming",
  citeseer-isreferencedby = "oai:CiteSeerPSU:80970",
  citeseer-references = "oai:CiteSeerPSU:276822;
                 \cite{oai:CiteSeerPSU:336117}; oai:CiteSeerPSU:327061;
                 oai:CiteSeerPSU:15714",
  annote =       "The Pennsylvania State University CiteSeer Archives",
  language =     "en",
  oai =          "oai:CiteSeerPSU:510392",
  rights =       "unrestricted",
  URL =          "http://eden.dei.uc.pt/~machado/research/pdf/2001/ISAS-2001.pdf",
  URL =          "http://citeseer.ist.psu.edu/510392.html",
  abstract =     "Genetic Programming involves the evolution of computer
                 programs, which are usually represented by trees
                 composed by functions and terminals. In order to assign
                 fitness, one must evaluate the programs, which is the
                 most time demanding step of GP. In nowadays standard
                 approaches, the evaluation involves an interpretation
                 step. To avoid this step, which significantly slows the
                 algorithm, some researchers evolve, directly, machine
                 code programs. An alternative approach is to build a
                 Genome Compiler, i.e. a system that transforms the
                 individual's trees in machine-code programs and
                 executes this code. Both techniques can bring huge
                 speed improvements. However, these approaches have some
                 shortcomings. In this paper we present GenCo: a
                 research project whose main goal is development of a
                 Genetic Programming Genome Compiler system, that
                 overcomes some of the drawbacks of current approaches,
                 enabling high speed improvements in a wider range of
                 domains. We will also present experimental results in a
                 programmatic compression task, in which GenCo was, on
                 average, 80 times faster than a standard C based GP
                 system.",
  notes =        "context of the International Conference CIMAF
                 2001.

                 Not verified

                 LilGP \cite{zonger:1996:lilgp} interpretation step
                 replaced by a compilation step. Lena image compression.
                 Claims in the region of 100000 to 1 million individuals
                 evaluated per second.

                 ",
}

Genetic Programming entries for Penousal Machado Andre Dias Amilcar Cardoso

Citations