On the behavioral diversity of random programs

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

@InProceedings{1277283,
  author =       "Moshe Looks",
  title =        "On the behavioral diversity of random programs",
  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 =        "1636--1642",
  address =      "London",
  URL =          "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p1636.pdf",
  DOI =          "doi:10.1145/1276958.1277283",
  publisher =    "ACM Press",
  publisher_address = "New York, NY, USA",
  month =        "7-11 " # jul,
  organisation = "ACM SIGEVO (formerly ISGEC)",
  keywords =     "genetic algorithms, genetic programming, empirical
                 Study, heuristics, optimisation, representation",
  abstract =     "Generating a random sampling of program trees with
                 specified function and terminal sets is the initial
                 step of many program evolution systems. I present a
                 theoretical and experimental analysis of the expected
                 distribution of uniformly sampled programs, guided by
                 algorithmic information theory. This analysis
                 demonstrates that increasing the sample size is often
                 an inefficient means of increasing the overall
                 diversity of program behaviours (outputs). A novel
                 sampling scheme (semantic sampling) is proposed that
                 exploits semantics to heuristically increase behavioral
                 diversity. An important property of the scheme is that
                 no calls of the problem-specific fitness function are
                 required. Its effectiveness at increasing behavioural
                 diversity is demonstrated empirically for Boolean
                 formulae. Furthermore, it is found to lead to
                 statistically significant improvements in performance
                 for genetic programming on parity and multiplexer
                 problems.",
  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 Moshe Looks

Citations