Learning Recursive Functions from Noisy Examples using Generic Genetic Programming

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

@InProceedings{wong:1996:lrfneGGP,

• author = "Man Leung Wong and Kwong Sak Leung",
• title = "Learning Recursive Functions from Noisy Examples using Generic Genetic Programming",
• booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference",
• editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo",
• year = "1996",
• month = "28--31 " # jul,
• keywords = "genetic algorithms, genetic programming",
• pages = "238--246",
• address = "Stanford University, CA, USA",
• publisher = "MIT Press",
• size = "9 pages",
• URL = "http://cptra.ln.edu.hk/~mlwong/conference/gp1996.pdf",
• URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap29.pdf",
• URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279",
• abstract = "One of the most important and challenging areas of research in evolutionary algorithms is the investigation of ways to successfully apply evolutionary algorithms to larger and more complicated problems. In this paper, we apply GGP (Generic Genetic Programming) to evolve general recursive functions for the even-n-parity problem from noisy training examples. GGP is very flexible and programs in various programming languages can be acquired. Moreover, it is powerful enough to handle context-sensitive information and domain-dependent knowledge. A number of experiments have been performed to determine the impact of noise in training examples on the speed of learning.",
• notes = "GP-96",

}

Genetic Programming entries for Man Leung Wong Kwong-Sak Leung

Citations