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
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;
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
DOI :
10.1109/CCDC.2008.4598058