Solving real-valued optimisation problems using cartesian genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4534

  author =       "James Alfred Walker and Julian Francis Miller",
  title =        "Solving real-valued optimisation problems using
                 cartesian genetic programming",
  booktitle =    "GECCO '07: Proceedings of the 9th annual conference on
                 Genetic and evolutionary computation",
  year =         "2007",
  editor =       "Dirk Thierens and Hans-Georg Beyer and 
                 Josh Bongard and Jurgen Branke and John Andrew Clark and 
                 Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and 
                 Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and 
                 Julian F. Miller and Jason Moore and Frank Neumann and 
                 Martin Pelikan and Riccardo Poli and Kumara Sastry and 
                 Kenneth Owen Stanley and Thomas Stutzle and 
                 Richard A Watson and Ingo Wegener",
  volume =       "2",
  isbn13 =       "978-1-59593-697-4",
  pages =        "1724--1730",
  address =      "London",
  URL =          "",
  DOI =          "doi:10.1145/1276958.1277295",
  publisher =    "ACM Press",
  publisher_address = "New York, NY, USA",
  month =        "7-11 " # jul,
  organisation = "ACM SIGEVO (formerly ISGEC)",
  keywords =     "genetic algorithms, genetic programming, Cartesian
                 Genetic Programming, embedded Cartesian Genetic
                 programming, evolutionary programming, modules, real
                 valued function optimisation",
  abstract =     "Classical Evolutionary Programming (CEP) and Fast
                 Evolutionary Programming (FEP) have been applied to
                 realvalued function optimisation. Both of these
                 techniques directly evolve the real-values that are the
                 arguments of the real-valued function. In this paper we
                 have applied a form of genetic programming called
                 Cartesian Genetic Programming (CGP) to a number of
                 real-valued optimisation benchmark problems. The
                 approach we have taken is to evolve a computer program
                 that controls a writing-head, which moves along and
                 interacts with a finite set of symbols that are
                 interpreted as real numbers, instead of manipulating
                 the real numbers directly. In other studies, CGP has
                 already been shown to benefit from a high degree of
                 neutrality. We hope to exploit this for real-valued
                 function optimisation problems to avoid being trapped
                 on local optima. We have also used an extended form of
                 CGP called Embedded CGP (ECGP) which allows the
                 acquisition, evolution and re-use of modules. The
                 effectiveness of CGP and ECGP are compared and
                 contrasted with CEP and FEP on the benchmark problems.
                 Results show that the new techniques are very
  notes =        "GECCO-2007 A joint meeting of the sixteenth
                 international conference on genetic algorithms
                 (ICGA-2007) and the twelfth annual genetic programming
                 conference (GP-2007).

                 ACM Order Number 910071",

Genetic Programming entries for James Alfred Walker Julian F Miller