Visualization of genetic lineages and inheritance information in genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  author =       "Bogdan Burlacu and Michael Affenzeller and 
                 Michael Kommenda and Stephan Winkler and Gabriel Kronberger",
  title =        "Visualization of genetic lineages and inheritance
                 information in genetic programming",
  booktitle =    "GECCO '13 Companion: Proceeding of the fifteenth
                 annual conference companion on Genetic and evolutionary
                 computation conference companion",
  year =         "2013",
  editor =       "Christian Blum and Enrique Alba and 
                 Thomas Bartz-Beielstein and Daniele Loiacono and 
                 Francisco Luna and Joern Mehnen and Gabriela Ochoa and 
                 Mike Preuss and Emilia Tantar and Leonardo Vanneschi and 
                 Kent McClymont and Ed Keedwell and Emma Hart and 
                 Kevin Sim and Steven Gustafson and 
                 Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and 
                 Nikolaus Hansen and Olaf Mersmann and Petr Posik and 
                 Heike Trautmann and Muhammad Iqbal and Kamran Shafi and 
                 Ryan Urbanowicz and Stefan Wagner and 
                 Michael Affenzeller and David Walker and Richard Everson and 
                 Jonathan Fieldsend and Forrest Stonedahl and 
                 William Rand and Stephen L. Smith and Stefano Cagnoni and 
                 Robert M. Patton and Gisele L. Pappa and 
                 John Woodward and Jerry Swan and Krzysztof Krawiec and 
                 Alexandru-Adrian Tantar and Peter A. N. Bosman and 
                 Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and 
                 David L. Gonzalez-Alvarez and 
                 Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and 
                 Kenneth Holladay and Tea Tusar and Boris Naujoks",
  isbn13 =       "978-1-4503-1964-5",
  keywords =     "genetic algorithms, genetic programming",
  pages =        "1351--1358",
  month =        "6-10 " # jul,
  organisation = "SIGEVO",
  address =      "Amsterdam, The Netherlands",
  DOI =          "doi:10.1145/2464576.2482714",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Many studies emphasise the importance of genetic
                 diversity and the need for an appropriate tuning of
                 selection pressure in genetic programming. Additional
                 important aspects are the performance and effects of
                 the genetic operators (crossover and mutation) on the
                 transfer and stabilisation of inherited information
                 blocks during the run of the algorithm. In this
                 context, different ideas about the usage of lineage and
                 genealogical information for improving genetic
                 programming have taken shape in the last decade.

                 Our work builds on those ideas by introducing an
                 evolution tracking framework for assembling
                 genealogical and inheritance graphs of populations. The
                 proposed approach allows detailed investigation of
                 phenomena related to building blocks, size evolution,
                 ancestry and diversity. We introduce the notion of
                 genetic fragments to represent sub-trees that are
                 affected by reproductive operators (mutation and
                 crossover) and present a methodology for tracking such
                 fragments using flexible similarity measures. A
                 fragment matching algorithm was designed to work on
                 both structural and semantic levels, allowing us to
                 gain insight into the exploratory and exploitative
                 behaviour of the evolutionary process.

                 The visualisation part which is the subject of this
                 paper integrates with the framework and provides an
                 easy way of exploring the population history. The paper
                 focuses on a case study in which we investigate the
                 evolution of a solution to a symbolic regression
                 benchmark problem.",
  notes =        "Also known as \cite{2482714} Distributed at

Genetic Programming entries for Bogdan Burlacu Michael Affenzeller Michael Kommenda Stephan M Winkler Gabriel Kronberger