Knowledge evolutionary algorithm based on granular computing

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  author =       "Yong-Qin Tao and Du-Wu Cui and Tai-Shan Yan",
  title =        "Knowledge evolutionary algorithm based on granular
  booktitle =    "IEEE Conference on Cybernetics and Intelligent
                 Systems, 2008",
  year =         "2008",
  month =        sep,
  pages =        "341--346",
  keywords =     "genetic algorithms, genetic programming, crossover
                 operator, evolutionary characteristics, granular
                 computing, knowledge evolutionary algorithm, knowledge
                 granulation, mutation operator, evolutionary
                 computation, knowledge engineering, mathematical
  DOI =          "doi:10.1109/ICCIS.2008.4670968",
  abstract =     "Granular computing makes mainly use of the information
                 of different granularities and hierarchies to solve
                 problems of the uncertain, fuzzy, imprecise, part true
                 and a number of information. This paper has analyzed
                 the evolutionary characteristics of knowledge
                 granulation and has proposed the evolution algorithm of
                 knowledge granulation (EAKG). EAKG algorithm applies
                 knowledge granulation to genetic programming and
                 carries through the evaluation according to coverage
                 degree and depends on degree to obtain some new rules.
                 In addition, this paper has also given the recursive
                 model of knowledge granulation evolution, crossover
                 operator and mutation operator, etc. Through the
                 experiments it has proved that it is the reasonable and
                 effective to carry out solution of knowledge evolution
                 with granule computing.",
  notes =        "Also known as \cite{4670968}",

Genetic Programming entries for Yong-Qin Tao Du-Wu Cui Tai-Shan Yan