• DocumentCode
    1918163
  • Title

    Implementing evolutionary self-organizing maps with the genetic of graph evolution theory

  • Author

    Chang, Maiga ; Heh, Jia-Sheng

  • Author_Institution
    Dept. of Inf. & Comput. Eng., Chung Yuan Christian Univ., Chung-li, Taiwan
  • Volume
    1
  • fYear
    2003
  • fDate
    20-24 July 2003
  • Firstpage
    462
  • Abstract
    This paper analyzes the genetic operations of a new evolution mechanism proposed by us for improving the capability to deal with graph-form solutions in the real world of genetic algorithms based on the theories of GAs and GPs. A prototype of graph evolution with genetic operations is implemented and applied to some graph-related systems with the Irish-student classification data. Evaluation between conventional optimization mechanisms and graph evolution theory is also made for proving the advantage of using graph evolution. Be notable is the graph evolution theory proposed in this paper can cover most applications of GAs and GPs.
  • Keywords
    genetic algorithms; graph theory; learning (artificial intelligence); pattern classification; self-organising feature maps; Irish student classification data; genetic algorithm; genetic operation; genetic programming; graph evolution theory; graph related system; optimization mechanisms; self organizing maps; Algorithm design and analysis; Biological cells; Character generation; Design optimization; Evolutionary computation; Genetic algorithms; Genetic programming; Prototypes; Self organizing feature maps; Tree graphs;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 2003. Proceedings of the International Joint Conference on
  • ISSN
    1098-7576
  • Print_ISBN
    0-7803-7898-9
  • Type

    conf

  • DOI
    10.1109/IJCNN.2003.1223390
  • Filename
    1223390