%0 Conference Paper %1 wong:1996:lrfneGGP %A Wong, Man Leung %A Leung, Kwong Sak %B Genetic Programming 1996: Proceedings of the First Annual Conference %C Stanford University, CA, USA %D 1996 %E Koza, John R. %E Goldberg, David E. %E Fogel, David B. %E Riolo, Rick L. %I MIT Press %K algorithms, genetic programming %P 238--246 %T Learning Recursive Functions from Noisy Examples using Generic Genetic Programming %U http://cognet.mit.edu/library/books/view?isbn=0262611279 %X 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.

@inproceedings{wong:1996:lrfneGGP, 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.}, added-at = {2008-06-19T17:35:00.000+0200}, address = {Stanford University, CA, USA}, author = {Wong, Man Leung and Leung, Kwong Sak}, biburl = {https://www.bibsonomy.org/bibtex/242dc50b5fab0b12a9b9352b2384c00e5/brazovayeye}, booktitle = {Genetic Programming 1996: Proceedings of the First Annual Conference}, editor = {Koza, John R. and Goldberg, David E. and Fogel, David B. and Riolo, Rick L.}, interhash = {1c996dfe3359f740789f2b2e37b31465}, intrahash = {42dc50b5fab0b12a9b9352b2384c00e5}, keywords = {algorithms, genetic programming}, month = {28--31 July}, notes = {GP-96}, pages = {238--246}, publisher = {MIT Press}, size = {9 pages}, timestamp = {2008-06-19T17:54:22.000+0200}, title = {Learning Recursive Functions from Noisy Examples using Generic Genetic Programming}, url = {http://cognet.mit.edu/library/books/view?isbn=0262611279}, year = 1996 }