An new immune genetic algorithm based on uniform design sampling

• Published in: Knowledge and information systems Vol. 31; no. 2; pp. 389 - 403
• Main Authors: Zhou, Ben-Da, Yao, Hong-Liang, Shi, Ming-Hua, Yue, Qin, Wang, Hao
• Format: Journal Article
• Language: English
• Published: London Springer-Verlag 01.05.2012; Springer; Springer Nature B.V
• Subjects: Applied sciences; Artificial intelligence; Bias; Chromosomes; Combinatorics; Combinatorics. Ordered structures; Computer Science; Computer science; control theory; systems; Convergence; Data Mining and Knowledge Discovery; Database Management; Design; Design engineering; Exact sciences and technology; Fault diagnosis; Genetic algorithms; Graph theory; Immune system; Immune systems; Information retrieval. Graph; Information Storage and Retrieval; Information systems; Information Systems and Communication Service; Information Systems Applications (incl.Internet); IT in Business; Learning and adaptive systems; Mathematics; Medical research; Optimization; Sampling; Sampling techniques; Sciences and techniques of general use; Short Paper; Strategy; Studies; Theoretical computing; China; Genetic algorithm (GA); Uniform design sampling (UDS); Artificial immune system (AIS); Maximum clique problem (MCP); Immune genetic algorithm based on uniform design sampling (UIGA); Artificial life; Graph clique; Bias; Graph theory; Sampling design; Global solution; Immune algorithm; Genetic algorithm; Optimal solution; Convergence rate; Clonal selection; Artificial intelligence; Numerical convergence; Immune system
• ISSN: 0219-1377; 0219-3116
• DOI: 10.1007/s10115-011-0476-3

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.