An new immune genetic algorithm based on uniform design sampling
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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.
KeywordsGenetic 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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- 2.Zhang L, Zhang B (2000) Research on the mechanism of genetic algorithms. J Softw (Chinese) 11(7): 945–952Google Scholar
- 3.Hua L, Wang Y (1978) Applications of number-theoretic methods in approximate analysis (Chinese). Science Press, BeijingGoogle Scholar
- 4.Zhang L, Zhang B (2001) Good point set based genetic algorithm. Chin J Comput (Chinese) 24(9): 917–922Google Scholar
- 7.Stepney S et al (2005) Conceptual frameworks for artificial immune systems. Int J Unconv Comput 1(3): 315–338Google Scholar
- 8.Li Z, Cheng J (2007) Immune good-point set genetic algorithm. Comput Eng Appl (Chinese) 43(28): 37–40Google Scholar
- 12.ftp://dimacs.rutgers.edu/pub/challenge/graph/benchmarks/clique/ [EB/OL] 20/3/2011
- 13.Bin J (2008) Basic research on artificial immune algorithm and its application (chinese). Central South University, ChangshaGoogle Scholar
- 14.De Jong KA (1975) An analysis of the behavior of a class of genetic adaptive systems. University of Michigan, Ann ArborGoogle Scholar