Self-focusing genetic programming for software optimisation

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

@InProceedings{Cody-Kenny:2013:GECCOcomp,

• author = "Brendan Cody-Kenny and Stephen Barrett",
• title = "Self-focusing genetic programming for software optimisation",
• 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 = "203--204",
• month = "6-10 " # jul,
• organisation = "SIGEVO",
• address = "Amsterdam, The Netherlands",
• DOI = "doi:10.1145/2464576.2464681",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• abstract = "Approaches in the area of Search Based Software Engineering (SBSE) have seen Genetic Programming (GP) algorithms applied to the optimisation of software. While the potential of GP for this task has been demonstrated, the complexity of real-world software code bases poses a scalability problem for its serious application. To address this scalability problem, we inspect a form of GP which incorporates a mechanism to focus operators to relevant locations within a program code base. When creating offspring individuals, we introduce operator node selection bias by allocating values to nodes within an individual. Offspring values are inherited and updated when a difference in behaviour between offspring and parent is found. We argue that this approach may scale to find optimal solutions in more complex code bases under further development.",
• notes = "Also known as \cite{2464681} Distributed at GECCO-2013.",

}

Genetic Programming entries for Brendan Cody-Kenny Stephen Barrett

Citations