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Knowledge and Information Systems

, Volume 31, Issue 2, pp 389–403 | Cite as

An new immune genetic algorithm based on uniform design sampling

  • Ben-Da ZhouEmail author
  • Hong-Liang Yao
  • Ming-Hua Shi
  • Qin Yue
  • Hao Wang
Short Paper

Abstract

The deficiencies of keeping population diversity, prematurity and low success rate of searching the global optimal solution are the shortcomings of genetic algorithm (GA). Based on the bias of samples in the uniform design sampling (UDS) point set, the crossover operation in GA is redesigned. Using the concentrations of antibodies in artificial immune system (AIS), the chromosomes concentration in GA is defined and the clonal selection strategy is designed. In order to solve the maximum clique problem (MCP), an new immune GA (UIGA) is presented based on the clonal selection strategy and UDS. The simulation results show that the UIGA provides superior solution quality, convergence rate, and other various indices to those of the simple and good point GA when solving MCPs.

Keywords

Genetic algorithm (GA) Uniform design sampling (UDS) Artificial immune system (AIS) Immune genetic algorithm based on uniform design sampling (UIGA) Maximum clique problem (MCP) 

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Copyright information

© Springer-Verlag London Limited 2012

Authors and Affiliations

  • Ben-Da Zhou
    • 1
    Email author
  • Hong-Liang Yao
    • 2
  • Ming-Hua Shi
    • 1
  • Qin Yue
    • 1
  • Hao Wang
    • 2
  1. 1.School of Applied MathematicsWest Anhui UniversityLu’anChina
  2. 2.School of Computer Science and TechnologyHefei University of TechnologyHefeiChina

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