On the Evolutionary Behavior of Genetic Programming with Constants Optimization

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Bogdan Burlacu and Michael Affenzeller and 
                 Michael Kommenda",
  title =        "On the Evolutionary Behavior of Genetic Programming
                 with Constants Optimization",
  booktitle =    "Computer Aided Systems Theory, EUROCAST 2013",
  year =         "2013",
  editor =       "Roberto Moreno-Diaz and Franz Pichler and 
                 Alexis Quesada-Arencibia",
  volume =       "8111",
  series =       "Lecture Notes in Computer Science",
  pages =        "284--291",
  address =      "Las Palmas de Gran Canaria, Spain",
  month =        feb # " 10-15",
  publisher =    "Springer",
  note =         "14th International Conference, Revised Selected
  keywords =     "genetic algorithms, genetic programming, evolutionary
                 behaviour, constant optimisation, symbolic regression,
                 algorithm analysis",
  bibdate =      "2013-12-16",
  bibsource =    "DBLP,
  isbn13 =       "978-3-642-53855-1",
  URL =          "http://dx.doi.org/10.1007/978-3-642-53856-8_36",
  DOI =          "doi:10.1007/978-3-642-53856-8_36",
  size =         "8 pages",
  abstract =     "Evolutionary systems are characterised by two
                 seemingly contradictory properties: robustness and
                 evolvability. Robustness is generally defined as an
                 organism's ability to withstand genetic perturbation
                 while maintaining its phenotype. Evolvability, as an
                 organism's ability to produce useful variation. In
                 genetic programming, the relationship between the two,
                 mediated by selection and variation-producing operators
                 (recombination and mutation), makes it difficult to
                 understand the behaviour and evolutionary dynamics of
                 the search process. In this paper, we show that a local
                 gradient-based constants optimisation step can improve
                 the overall population evolvability by inducing a
                 beneficial structure-preserving bias on selection,
                 which in the long term helps the process maintain
                 diversity and produce better solutions.",

Genetic Programming entries for Bogdan Burlacu Michael Affenzeller Michael Kommenda