Sub-Machine-Code GP: New Results and Extensions

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{poli:1999:smcGP:nre,
  author =       "Riccardo Poli",
  title =        "Sub-Machine-Code GP: New Results and Extensions",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'99",
  year =         "1999",
  editor =       "Riccardo Poli and Peter Nordin and 
                 William B. Langdon and Terence C. Fogarty",
  volume =       "1598",
  series =       "LNCS",
  pages =        "65--82",
  address =      "Goteborg, Sweden",
  publisher_address = "Berlin",
  month =        "26-27 " # may,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-65899-8",
  URL =          "http://www.cs.essex.ac.uk/staff/poli/papers/Poli-EUROGP1999.pdf",
  URL =          "http://citeseer.ist.psu.edu/329358.html",
  URL =          "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1598&spage=65",
  DOI =          "doi:10.1007/3-540-48885-5_6",
  abstract =     "Sub-machine-code GP (SMCGP) is a technique to speed up
                 genetic programming (GP) and to extend its scope based
                 on the idea of exploiting the internal parallelism of
                 sequential CPUs. In previous work [20] we have shown
                 examples of applications of this technique to the
                 evolution of parallel programs and to the parallel
                 evaluation of 32 or 64 fitness cases per program
                 execution in Boolean classification problems. After
                 recalling the basic features of SMCGP, in this paper we
                 first apply this technique to the problem of evolving
                 parallel binary multipliers. Then we describe how SMCGP
                 can be extended to process multiple fitness cases per
                 program execution in continuous symbolic regression
                 problems where inputs and outputs are real-valued
                 numbers, reporting experimental results on a quartic
                 polynomial approximation task.",
  notes =        "EuroGP'99, part of \cite{poli:1999:GP}

                 SMC allows multiple fitness cases to be processed in
                 one go. New demo on evolving multiply and on continious
                 regression problems.",
}

Genetic Programming entries for Riccardo Poli

Citations