A Genome Compiler for High Performance Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4340

  author =       "Alex Fukunaga and Andre Stechert and Darren Mutz",
  title =        "A Genome Compiler for High Performance Genetic
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and 
                 Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and 
                 David B. Fogel and Max H. Garzon and 
                 David E. Goldberg and Hitoshi Iba and Rick Riolo",
  pages =        "86--94",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-548-7",
  URL =          "http://fukunaga.bol.ucla.edu/gp98-compiler.pdf",
  URL =          "http://citeseer.ist.psu.edu/fukunaga98genome.html",
  abstract =     "Genetic Programming is very computationally expensive.
                 For most applications, the vast majority of time is
                 spent evaluating candidate solutions, so it is
                 desirable to make individual evaluation as efficient as
                 possible. We describe a genome compiler which compiles
                 s-expressions to machine code, resulting in significant
                 speedup of individual evaluations over standard GP
                 systems. Based on performance results with symbolic
                 regression, we show that the execution of the genome
                 compiler system is...",
  notes =        "GP-98

                 Thu, 25 Jun 1998 10:31:36 PDT We've recently developed
                 a gp system based on lil-gp which evolves s-expressions
                 and compiles it to machine code (specifically, Sparc
                 machine code) to speed up evaluation. In our system,
                 we've found that the overhead of compilation is
                 negligible, since the vast majority of the time spent
                 in execution in an s-expression interpreter (in our
                 case, the lil-gp interpreter) is consumed by the
                 recursive traversal of the tree.

                 A full description, comparisons with previous
                 GP-compiler systems and some experimental results with
                 symbolic regression and image compression are

Genetic Programming entries for Alex S Fukunaga Andre Stechert Darren Mutz