DocumentCode
2567000
Title
A sort-based improved real-code genetic algorithm
Author
Gao Xian-wen ; Zhang Guo-hui
Author_Institution
Coll. of Inf. Sci. & Eng., Northeastern Univ., Shenyang
fYear
2008
fDate
2-4 July 2008
Firstpage
3877
Lastpage
3881
Abstract
As an adaptive global optimize method by probabilistic search, genetic algorithm had been comprehensively used in many engineering realms. But some disadvantages of this method such as slow convergence speed and local optimization confined further applications. Improved genetic algorithm in speed of convergence and the rate of obtain the optimal solution improved significantly by combine and sort parent-child generations, applying improved proportional selection, anticipative crossover, additive gauss-mutation and so on. Improved genetic algorithm has excellent performance, good universality, suitable for promotion and application.
Keywords
genetic algorithms; adaptive global optimize method; probabilistic search; sort-based improved real-code genetic algorithm; Educational institutions; Electronic mail; Gaussian processes; Genetic algorithms; Genetic engineering; Information science; Optimization methods; Proportional control; Anticipative Crossover; Gauss-mutation; Genetic Algorithm; Improved Proportional Selection; Real-code;
fLanguage
English
Publisher
ieee
Conference_Titel
Control and Decision Conference, 2008. CCDC 2008. Chinese
Conference_Location
Yantai, Shandong
Print_ISBN
978-1-4244-1733-9
Electronic_ISBN
978-1-4244-1734-6
Type
conf
DOI
10.1109/CCDC.2008.4598058
Filename
4598058
Link To Document