DocumentCode :
2091759
Title :
A dual population genetic algorithm with learning scheme
Author :
Yang, Weinan ; Bo, Yarning
Author_Institution :
Coll. of Electron. Sci. & Eng., Nanjing Univ. of Posts & Telecommun., Nanjing, China
fYear :
2010
fDate :
11-14 Nov. 2010
Firstpage :
633
Lastpage :
636
Abstract :
In order to overcome the shortcomings of some evolutionary algorithms with slow or premature convergence, such as standard genetic algorithm (SGA), a novel dual population genetic algorithm with learning scheme (SDGA) is proposed in this paper. Based on the SGA, the population is divided into two groups. The offspring produced by crossover can join in one of the two groups, or be discarded by fitness comparisons after learning or mutation. With learning scheme and suitable member updating rules, the proposed algorithm is capable of outstanding global optimization. Numerical results show that the SDGA has a high success rate and low computation consuming for global optimizations. Moreover, the SDGA can give high accuracy solutions for high dimensional problems with lower computing costs.
Keywords :
convergence; genetic algorithms; dual population genetic algorithm; evolutionary algorithm; global optimization; learning scheme; premature convergence; standard genetic algorithm; Broadband communication; Robustness; Transforms; Wireless communication; dual population; global optimization; individual updating; learning scheme; multi-dimensional problem;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Communication Technology (ICCT), 2010 12th IEEE International Conference on
Conference_Location :
Nanjing
Print_ISBN :
978-1-4244-6868-3
Type :
conf
DOI :
10.1109/ICCT.2010.5688948
Filename :
5688948
Link To Document :
بازگشت