免疫系を用いた遺伝的プログラミングによる多峰性探索  [in Japanese] Multimodal Search with Immune Based Genetic Programming  [in Japanese]

Access this Article

Search this Article

Author(s)

    • 伊庭 斉志 IBA Hitoshi
    • 東京大学大学院新領域創成科学研究科 Department of Frontier Informatics, Graduate School of Frontier Sciences, University of Tokyo

Abstract

Artificial Immune System has been regarded an effective powerful optimization framework because of its powerful information processing capabilities. Natural immune system has many features such as memorizing ability, singularity against antigens, flexibility against dynamically changing environments, and diversity of antibody. Up to now, several algorithms inspired by these immune features have been proposed and applied to many problems. However, Genetic Programming with immune features which is capable of solving multimodal problems has not been proposed. This paper proposes an optimization algorithm named Multimodal Search Genetic Programming (MSGP), which extends GP by introducing the immunological feature so as to solve the problems with multimodal fitness landscape. We empirically show the effectiveness of our approach by applying the algorithm to the gene classification problem and the HP protein folding problem.

Journal

  • Transactions of the Japanese Society for Artificial Intelligence

    Transactions of the Japanese Society for Artificial Intelligence 21, 176-183, 2006-11-01

    The Japanese Society for Artificial Intelligence

References:  31

Cited by:  3

Codes

  • NII Article ID (NAID)
    10022006157
  • NII NACSIS-CAT ID (NCID)
    AA11579226
  • Text Lang
    JPN
  • Article Type
    Journal Article
  • ISSN
    13460714
  • NDL Article ID
    8686433
  • NDL Source Classification
    ZM13(科学技術--科学技術一般--データ処理・計算機)
  • NDL Call No.
    Z74-C589
  • Data Source
    CJP  CJPref  NDL  J-STAGE 
Page Top