Evolving recursive functions for the even-parity problem using genetic programming

Man Leung WONG, K. S. LEUNG

    Research output: Book Chapters | Papers in Conference ProceedingsBook Chapter

    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 chapter. we apply GGP (Generic Genetic Programming) to evolve general recursive functions for the even-n-parity problem. 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. This knowledge can be used to accelerate the learning speed and/or improve the quality of the programs induced. A number of experiments have been performed to determine the impact of domain-specific knowledge on the speed of learning.
    Original languageEnglish
    Title of host publicationAdvances in genetic programming
    EditorsPeter J. ANGELINE, Kenneth E. KLNNEAR
    PublisherMIT Press
    Pages221-240
    Number of pages20
    Volume2
    ISBN (Print)9780262011587
    DOIs
    Publication statusPublished - 1996

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    WONG, M. L., & LEUNG, K. S. (1996). Evolving recursive functions for the even-parity problem using genetic programming. In P. J. ANGELINE, & K. E. KLNNEAR (Eds.), Advances in genetic programming (Vol. 2, pp. 221-240). MIT Press. https://doi.org/10.7551/mitpress/1109.003.0016