A Knowledge-Evolution Strategy Based on Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4340

  author =       "Chan-Sheng Kuo and Tzung-Pei Hong and 
                 Chuen-Lung Chen",
  title =        "A Knowledge-Evolution Strategy Based on Genetic
  booktitle =    "International Conference on Convergence and Hybrid
                 Information Technology, ICHIT '08",
  year =         "2008",
  month =        aug,
  pages =        "43--48",
  keywords =     "genetic algorithms, genetic programming, evolutionary
                 process, integrated classification tree, knowledge
                 management, knowledge-evolution strategy, learning
                 efficiency, organizational need, knowledge management,
                 organisational aspects, trees (mathematics)",
  DOI =          "doi:10.1109/ICHIT.2008.169",
  abstract =     "Knowledge evolution is an important issue in knowledge
                 management since enterprises face keen competition and
                 need to keep the latest knowledge with time in an
                 organization. In this paper, we proposed a GP-based
                 knowledge-evolution framework to search for a good
                 integrated classification tree with different evolving
                 time points. The proposed approach can learn the
                 evolving knowledge, integrating original and new
                 knowledge, to deal properly with the organizational
                 need for updating the latest knowledge as time goes on
                 in a dynamic environment. In addition, we developed the
                 initial population, consisting of four proportions, to
                 accomplish suitable diversity and thus raise the search
                 range as well as next learning efficiency in the
                 evolutionary process.",
  notes =        "Also known as \cite{4622798}",

Genetic Programming entries for Chan-Sheng Kuo Tzung-Pei Hong Samuel Chuen-Lung Chen