DocumentCode
507571
Title
A New Adaptive Immune Genetic Algorighm
Author
Chang, Zheng ; Zhu, Guangming
Author_Institution
Shandong Univ. of Technol., Zibo, China
Volume
1
fYear
2009
fDate
Nov. 30 2009-Dec. 1 2009
Firstpage
395
Lastpage
397
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;
fLanguage
English
Publisher
ieee
Conference_Titel
Knowledge Acquisition and Modeling, 2009. KAM '09. Second International Symposium on
Conference_Location
Wuhan
Print_ISBN
978-0-7695-3888-4
Type
conf
DOI
10.1109/KAM.2009.21
Filename
5362148
Link To Document