Evolution Tracking in Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3949

@InProceedings{Burlacu:2012:EMSS,
  author =       "Bogdan Burlacu and Michael Affenzeller and 
                 Michael Kommenda and Stephan M. Winkler and 
                 Gabriel Kronberger",
  title =        "Evolution Tracking in Genetic Programming",
  booktitle =    "The 24th European Modeling and Simulation Symposium,
                 EMSS 2012",
  year =         "2012",
  editor =       "Emilio Jimenez and Boris Sokolov",
  address =      "Vienna, Austria",
  month =        sep # ", 19-21",
  keywords =     "genetic algorithms, genetic programming, tree
                 fragments, evolutionary dynamics, schema theory,
                 population diversity, bloat, introns",
  URL =          "http://research.fh-ooe.at/en/publication/3444",
  URL =          "http://research.fh-ooe.at/files/publications/3444_EMSS_2012_Burlacu.pdf",
  size =         "4 pages",
  abstract =     "Much effort has been put into understanding the
                 artificial evolutionary dynamics within genetic
                 programming (GP). However, the details are yet unclear
                 so far, as to which elements make GP so powerful. This
                 paper presents an attempt to study the evolution of a
                 population of computer programs using HeuristicLab. A
                 newly developed methodology for recording heredity
                 information, based on a general conceptual framework of
                 evolution, is employed for the analysis of algorithm
                 behaviour on a symbolic regression benchmark problem.
                 In our example, we find the complex interplay between
                 selection and crossover to be the cause for size
                 increase in the population, as the average amount of
                 genetic information transmitted from parents to
                 offspring remains constant and independent of run
                 constraints (i.e., tree size and depth limits).
                 Empirical results reveal many interesting details and
                 confirm the validity and generality of our approach, as
                 a tool for understanding the complex aspects of GP.",
  notes =        "EMSS_80 University of Applied Sciences Upper Austria -
                 Austria http://www.msc-les.org/conf/emss2012/index.htm
                 http://www.m-s-net.org/conf/i3m2012_program.pdf",
}

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

Citations