Efficient Evolution of Parallel Binary Multipliers and Continuous Symbolic Regression Expressions with Sub-Machine-Code GP

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

@TechReport{poli:CSRP-98-19,
  author =       "Riccardo Poli",
  title =        "Efficient Evolution of Parallel Binary Multipliers and
                 Continuous Symbolic Regression Expressions with
                 Sub-Machine-Code {GP}",
  institution =  "University of Birmingham, School of Computer Science",
  number =       "CSRP-98-19",
  month =        dec,
  year =         "1998",
  file =         "/1998/CSRP-98-19.ps.gz",
  URL =          "ftp://ftp.cs.bham.ac.uk/pub/tech-reports/1998/CSRP-98-19.ps.gz",
  ftpaddress =   "ftp.cs.bham.ac.uk",
  reportfilename = "pub/tech-reports/1998/CSRP-98-19.ps.gz",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "Sub-machine-code GP (SMCGP) is a new 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 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.",
}

Genetic Programming entries for Riccardo Poli

Citations