DocumentCode
2938900
Title
Application on Express Delivery of an Immune Genetic Algorithm Based on Machine Learning
Author
Zheng, Chang ; Guangming, Zhu
Author_Institution
Shandong Univ. of Technol., Zibo, China
Volume
2
fYear
2009
fDate
12-14 Dec. 2009
Firstpage
165
Lastpage
167
Abstract
A new set of immune genetic algorithm is designed to solve express delivery path optimization problem, which introduces static propagation principle and machine learning theory to the immune genetic algorithm. Using adaptive vaccines, enhance individual immunity, and increase the average fitness value of stocks, so as to effectively prevent the loss of the optimal solution to narrow the search space, making the speed of evolution speeded up, enabling the system to get the optimal solution in a very short time. After verification, the algorithm is much higher accuracy than the simple genetic algorithm, and the number of iterations to get a stable solution is significantly reduced.
Keywords
artificial immune systems; genetic algorithms; iterative methods; learning (artificial intelligence); search problems; adaptive vaccines; enhance individual immunity; evolution speed; express delivery path optimization problem; immune genetic algorithm; iterations; machine learning theory; search space; static propagation principle; Algorithm design and analysis; Biological cells; Companies; Computational intelligence; Design optimization; Genetic algorithms; Job shop scheduling; Machine learning; Machine learning algorithms; Vaccines; immune genetic algorithm; machine learning; static propagation;
fLanguage
English
Publisher
ieee
Conference_Titel
Computational Intelligence and Design, 2009. ISCID '09. Second International Symposium on
Conference_Location
Changsha
Print_ISBN
978-0-7695-3865-5
Type
conf
DOI
10.1109/ISCID.2009.189
Filename
5370881
Link To Document