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
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;
Conference_Titel :
Computational Intelligence and Design, 2009. ISCID '09. Second International Symposium on
Conference_Location :
Changsha
Print_ISBN :
978-0-7695-3865-5
DOI :
10.1109/ISCID.2009.189