Title :
A New Adaptive Immune Genetic Algorighm
Author :
Chang, Zheng ; Zhu, Guangming
Author_Institution :
Shandong Univ. of Technol., Zibo, China
fDate :
Nov. 30 2009-Dec. 1 2009
Abstract :
The theories of machine-learning are applied to the immune genetic algorithm. Chromosomes´ immunity is enhanced and the average fitness of chromosomes is improved by using adaptive vaccine, so as to avoid the loss of the best solution, shrink the searching space and speed up the evolution, then the best solution can be get earlier. At the same time, the results are compared with each other through the optimization calculation of the modified immune genetic algorithm and the traditional genetic algorithm in solving classic 3 × 3 JSP problem.
Keywords :
genetic algorithms; job shop scheduling; learning (artificial intelligence); adaptive immune genetic algorithm; adaptive vaccine; chromosomes immunity; job shop scheduling problem; machine learning; Biological cells; Encoding; Flowcharts; Genetic algorithms; Machine learning; Machine learning algorithms; Scheduling algorithm; Space technology; Time factors; Vaccines;
Conference_Titel :
Knowledge Acquisition and Modeling, 2009. KAM '09. Second International Symposium on
Conference_Location :
Wuhan
Print_ISBN :
978-0-7695-3888-4
DOI :
10.1109/KAM.2009.21
