Learning Recursive Functions from Noisy Examples using Generic Genetic Programming

Man Leung WONG, Kwong Sak LEUNG

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)

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.
Original languageEnglish
Title of host publicationProceedings of the 1st annual conference on genetic programming
PublisherMIT Press
Pages238-246
ISBN (Print)9780262611275
DOIs
Publication statusPublished - Jul 1996
Externally publishedYes
Event1st annual conference on genetic programming - Stanford University, California, United States
Duration: 28 Jul 199631 Jul 1996
https://dl.acm.org/doi/proceedings/10.5555/1595536

Conference

Conference1st annual conference on genetic programming
CountryUnited States
CityCalifornia
Period28/07/9631/07/96
Internet address

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  • Cite this

    WONG, M. L., & LEUNG, K. S. (1996). Learning Recursive Functions from Noisy Examples using Generic Genetic Programming. In Proceedings of the 1st annual conference on genetic programming (pp. 238-246). MIT Press. https://doi.org/10.5555/1595536.1595566