• DocumentCode
    2309579
  • Title

    SOM-based optimization

  • Author

    Su, Mu-Chun ; Zhao, Yu-Xiang ; Lee, Jonathan

  • Author_Institution
    Dept. of Comput. Sci. & Inf. Eng., Nat. Central Univ., Taiwan
  • Volume
    1
  • fYear
    2004
  • fDate
    25-29 July 2004
  • Lastpage
    786
  • Abstract
    A new approach to optimization problems based on the self-organizing feature maps is proposed. We name the new optimization algorithm the SOM-based optimization (SOMO) algorithm. Through the self-organizing process, good solutions to an optimization problem can be simultaneously explored and exploited. An additional advantage of the algorithm is that the outputs of the neural network allow us to transform a multi-dimensional fitness landscape into a three-dimensional projected fitness landscape. Several simulations are used to illustrate the effectiveness of the proposed optimization algorithm.
  • Keywords
    genetic algorithms; self-organising feature maps; genetic algorithm; multidimensional fitness landscape; neural network; optimization algorithm; self-organizing feature maps; Biological neural networks; Brain modeling; Clustering algorithms; Computational modeling; Computer science; Evolutionary computation; Genetic algorithms; Genetic mutations; Genetic programming; Particle swarm optimization;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on
  • ISSN
    1098-7576
  • Print_ISBN
    0-7803-8359-1
  • Type

    conf

  • DOI
    10.1109/IJCNN.2004.1380019
  • Filename
    1380019