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


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.


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


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    El Akadi A, Amine A et al (2011) A two-stage gene selection scheme utilizing MRMR filter and GA wrapper. Knowl Inf Syst 26(3): 487–500CrossRefGoogle Scholar
  2. 2.
    Zhang L, Zhang B (2000) Research on the mechanism of genetic algorithms. J Softw (Chinese) 11(7): 945–952Google Scholar
  3. 3.
    Hua L, Wang Y (1978) Applications of number-theoretic methods in approximate analysis (Chinese). Science Press, BeijingGoogle Scholar
  4. 4.
    Zhang L, Zhang B (2001) Good point set based genetic algorithm. Chin J Comput (Chinese) 24(9): 917–922Google Scholar
  5. 5.
    Zhang R-C, Wang Z-J (1996) Uniform design sampling and its fine properties (Chinese). Chin J Appl Probab Stat 12(4): 337–347zbMATHGoogle Scholar
  6. 6.
    Gong M, Jiao L et al (2010) Immune algorithm with orthogonal design based initialization, cloning, and selection for global optimization. Knowl Inf Syst 25: 523–549CrossRefGoogle Scholar
  7. 7.
    Stepney S et al (2005) Conceptual frameworks for artificial immune systems. Int J Unconv Comput 1(3): 315–338Google Scholar
  8. 8.
    Li Z, Cheng J (2007) Immune good-point set genetic algorithm. Comput Eng Appl (Chinese) 43(28): 37–40Google Scholar
  9. 9.
    Singh A, Gupta AK (2006) A hybrid heuristic for the maximum clique problem. J Heuristics 12(1–2): 5–22zbMATHCrossRefGoogle Scholar
  10. 10.
    Pullan W, Hoos HH (2006) Dynamic local search for the maximum clique problem. J Artif Intell Res 25: 159–185zbMATHGoogle Scholar
  11. 11.
    Balas E, Niehaus W (1998) Optimized crossover-based genetic algorithms for the maximum cardinality and maximum weight clique problems. J Heuristics 4(2): 107–122zbMATHCrossRefGoogle Scholar
  12. 12.
  13. 13.
    Bin J (2008) Basic research on artificial immune algorithm and its application (chinese). Central South University, ChangshaGoogle Scholar
  14. 14.
    De Jong KA (1975) An analysis of the behavior of a class of genetic adaptive systems. University of Michigan, Ann ArborGoogle Scholar

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

Personalised recommendations