- author = "William B. Langdon",
- title = "Generalisation in Genetic Programming",
- booktitle = "GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation",
- year = "2011",
- editor = "Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger",
- isbn13 = "978-1-4503-0690-4",
- pages = "205",
- month = "12-16 " # jul,
- organisation = "SIGEVO",
- address = "Dublin, Ireland",
- publisher = "ACM",
- keywords = "genetic algorithms, genetic programming: Poster, AI, Problem Solving, Control Methods, and Search, Heuristic methods, Theory, 11-Mux, GPGPU, GPU, bloat, over fitting",
-
URL = "
http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/langdon_2011_gecco.pdf",
-
URL = "
http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/langdon_2011_gecco.ps.gz",
-
DOI = "
10.1145/2001858.2001972",
- size = "1 page",
- abstract = "The cost of optimisation can be reduced by evaluating the value of candidate designs on only a fraction of all possible fitness cases. We show how genetic programming (GP) can avoid overfitting and evolve general solutions from test suites as small as just one dynamic training case, thereby greatly reducing search effort.",
-
notes = "Fuller version in \cite{langdon:2011:geccoRN} Also
known as \cite{2001972} Distributed on CD-ROM at
GECCO-2011.
ACM Order Number 910112.",
