A new methodology for the GP theory toolbox

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Bassett:2012:GECCO,
  author =       "Jeffrey Bassett and Uday Kamath and 
                 Kenneth {De Jong}",
  title =        "A new methodology for the GP theory toolbox",
  booktitle =    "GECCO '12: Proceedings of the fourteenth international
                 conference on Genetic and evolutionary computation
                 conference",
  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 =        "719--726",
  keywords =     "genetic algorithms, genetic programming",
  month =        "7-11 " # jul,
  organisation = "SIGEVO",
  address =      "Philadelphia, Pennsylvania, USA",
  DOI =          "doi:10.1145/2330163.2330264",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Recently Quantitative Genetics has been successfully
                 employed to understand and improve operators in some
                 Evolutionary Algorithms (EAs) implementations. This
                 theory offers a phenotypic view of an algorithm's
                 behavior at a population level, and suggests new ways
                 of quantifying and measuring concepts such as
                 exploration and exploitation. In this paper, we extend
                 the quantitative genetics approach for use with Genetic
                 Programming (GP), adding it to the set of GP analysis
                 techniques. We use it in combination with some existing
                 diversity and bloat measurement tools to measure,
                 analyze and predict the evolutionary behavior of
                 several GP algorithms. GP specific benchmark problems,
                 such as ant trail and symbolic regression, are used to
                 provide new insight into how various evolutionary
                 forces work in combination to affect the search
                 process. Finally, using the tools, a multivariate
                 phenotypic crossover operator is designed to both
                 improve performance and control bloat on the difficult
                 ant trail problem.",
  notes =        "Also known as \cite{2330264} 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 Jeffrey K Bassett Uday Kamath Kenneth De Jong

Citations