An investigation of local patterns for estimation of distribution genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4504

  author =       "Erik Hemberg and Kalyan Veeramachaneni and 
                 James McDermott and Constantin Berzan and Una-May O'Reilly",
  title =        "An investigation of local patterns for estimation of
                 distribution genetic programming",
  booktitle =    "GECCO '12: Proceedings of the fourteenth international
                 conference on Genetic and evolutionary computation
  year =         "2012",
  editor =       "Terry Soule and Anne Auger and Jason Moore and 
                 David Pelta and Christine Solnon and Mike Preuss and 
                 Alan Dorin and Yew-Soon Ong and Christian Blum and 
                 Dario Landa Silva and Frank Neumann and Tina Yu and 
                 Aniko Ekart and Will Browne and Tim Kovacs and 
                 Man-Leung Wong and Clara Pizzuti and Jon Rowe and Tobias Friedrich and 
                 Giovanni Squillero and Nicolas Bredeche and 
                 Stephen L. Smith and Alison Motsinger-Reif and Jose Lozano and 
                 Martin Pelikan and Silja Meyer-Nienberg and 
                 Christian Igel and Greg Hornby and Rene Doursat and 
                 Steve Gustafson and Gustavo Olague and Shin Yoo and 
                 John Clark and Gabriela Ochoa and Gisele Pappa and 
                 Fernando Lobo and Daniel Tauritz and Jurgen Branke and 
                 Kalyanmoy Deb",
  isbn13 =       "978-1-4503-1177-9",
  pages =        "767--774",
  keywords =     "genetic algorithms, genetic programming",
  month =        "7-11 " # jul,
  organisation = "SIGEVO",
  address =      "Philadelphia, Pennsylvania, USA",
  DOI =          "doi:10.1145/2330163.2330270",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "We present an improved estimation of distribution
                 (EDA) genetic programming (GP) algorithm which does not
                 rely upon a prototype tree. Instead of using a
                 prototype tree, Operator-Free Genetic Programming
                 learns the distribution of ancestor node chains,
                 {"}n-grams{"}, in a fit fraction of each generation's
                 population. It then uses this information, via
                 sampling, to create trees for the next generation.
                 Ancestral n-grams are used because an analysis of a GP
                 run conducted by learning depth first graphical models
                 for each generation indicated their emergence as
                 substructures of conditional dependence. We are able to
                 show that our algorithm, without an operator and a
                 prototype tree, achieves, on average, performance close
                 to conventional tree based crossover GP on the problem
                 we study. Our approach sets a direction for
                 pattern-based EDA GP which off ers better tractability
                 and improvements over GP with operators or EDAs using
                 prototype trees.",
  notes =        "Also known as \cite{2330270} GECCO-2012 A joint
                 meeting of the twenty first international conference on
                 genetic algorithms (ICGA-2012) and the seventeenth
                 annual genetic programming conference (GP-2012)",

Genetic Programming entries for Erik Hemberg Kalyan Veeramachaneni James McDermott Constantin Berzan Una-May O'Reilly