A new methodology for the GP theory toolbox

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@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