An entropy-based multi-population genetic algorithm and its application

  • Authors:

  • Chun-lian Li;Yu sun;Yan-shen Guo;Feng-ming Chu;Zong-ru Guo

  • Affiliations:

  • School of Computer Science, Changchun University, Changchun, China;Institute of Special Education, Changchun University, Changchun, China;Institute of Material Medical Chinese Academy of Medical Sciences &, Peking Union Medical College, Beijing, China;Institute of Material Medical Chinese Academy of Medical Sciences &, Peking Union Medical College, Beijing, China;Institute of Material Medical Chinese Academy of Medical Sciences &, Peking Union Medical College, Beijing, China

  • Venue:
  • ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
  • Year:
  • 2005

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Abstract

An improved genetic algorithm based on information entropy is presented in this paper. A new iteration scheme in conjunction with multi-population genetic strategy, entropy-based searching technique with narrowing down space and the quasi-exact penalty function is developed to solve nonlinear programming problems with equality and inequality constraints. A specific strategy of reserving the most fitness member with evolutionary historic information is effectively used to approximate the solution of the nonlinear programming problems to the global optimization. Numerical examples and an application in molecular docking demonstrate its accuracy and efficiency.