Generalisation of the limiting distribution of program sizes in tree-based genetic programming and analysis of its effects on bloat

Created by W.Langdon from gp-bibliography.bib Revision:1.4216

  author =       "Stephen Dignum and Riccardo Poli",
  title =        "Generalisation of the limiting distribution of program
                 sizes in tree-based genetic programming and analysis of
                 its effects on bloat",
  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 =        "1588--1595",
  address =      "London",
  URL =          "",
  DOI =          "doi:10.1145/1276958.1277277",
  publisher =    "ACM Press",
  publisher_address = "New York, NY, USA",
  month =        "7-11 " # jul,
  organisation = "ACM SIGEVO (formerly ISGEC)",
  keywords =     "genetic algorithms, genetic programming, bloat,
                 crossover Bias, initialisation, program Size
  abstract =     "Recent research [1] has found that standard sub-tree
                 crossover with uniform selection of crossover points,
                 in the absence of fitness pressure, pushes a population
                 of GP trees towards a Lagrange distribution of tree
                 sizes. However, the result applied to the case of
                 single arity function plus leaf node combinations,
                 e.g., unary, binary, ternary, etc trees only. In this
                 paper we extend those findings and show that the same
                 distribution is also applicable to the more general
                 case where the function set includes functions of mixed
                 arities. We also provide empirical evidence that
                 strongly corroborates this generalisation. Both
                 predicted and observed results show a distinct bias
                 towards the sampling of shorter programs irrespective
                 of the mix of function arities used. Practical
                 applications and implications of this knowledge are
                 investigated with regard to search efficiency and
                 program bloat. Work is also presented regarding the
                 applicability of the theory to the traditional
                 0.90-function 0.10-terminal crossover node selection
  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 Stephen Dignum Riccardo Poli