Semantic Analysis of Program Initialisation in Genetic Programming

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

@Article{Beadle:2009:GPEM,
  author =       "Lawrence Beadle and Colin G. Johnson",
  title =        "Semantic Analysis of Program Initialisation in Genetic
                 Programming",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2009",
  volume =       "10",
  number =       "3",
  pages =        "307--337",
  month =        sep,
  keywords =     "genetic algorithms, genetic programming, Program
                 initialisation, Program semantics, Program structure",
  ISSN =         "1389-2576",
  URL =          "http://www.springerlink.com/content/yn5p45723l6tr487",
  DOI =          "doi:10.1007/s10710-009-9082-5",
  abstract =     "Population initialisation in genetic programming is
                 both easy, because random combinations of syntax can be
                 generated straightforwardly, and hard, because these
                 random combinations of syntax do not always produce
                 random and diverse program behaviours. In this paper we
                 perform analyses of behavioural diversity, the size and
                 shape of starting populations, the effects of purely
                 semantic program initialisation and the importance of
                 tree shape in the context of program initialisation. To
                 achieve this, we create four different algorithms, in
                 addition to using the traditional ramped half and half
                 technique, applied to seven genetic programming
                 problems. We present results to show that varying the
                 choice and design of program initialisation can
                 dramatically influence the performance of genetic
                 programming. In particular, program behaviour and
                 evolvable tree shape can have dramatic effects on the
                 performance of genetic programming. The four algorithms
                 we present have different rates of success on different
                 problems.",
}

Genetic Programming entries for Lawrence Beadle Colin G Johnson

Citations