DocumentCode :
3727469
Title :
An Improved Many Worlds Quantum Genetic Algorithm
Author :
Dan Li; Junsuo Zhao; Heng Zhang; Peng Qiao; Jiayu Zhuang
Author_Institution :
Science and Technology on Integrated Information System Laboratory, Institute of Software, Chinese Academy of Sciences, Beijing 100190, China
fYear :
2015
Firstpage :
210
Lastpage :
214
Abstract :
An Improved Many Worlds Quantum Genetic Algorithm (IMWQGA) was proposed aiming at the shortcomings of the Quantum Genetic Algorithm, such as the multimodal function optimization problems easily falling into the local optimum and vulnerability to premature convergence. Using the concept of Many Worlds and the derivative way of parallel worlds´ parallel evolution, we propose to update the population according to the main body and adopt the transition methods, such as parallel transition, backtracking, travel forth and so on. In addition, the quantum training operator and the combinatorial optimization operator as new operators of quantum genetic algorithm were also proposed.
Keywords :
"Genetic algorithms","Encoding","Logic gates","Sociology","Statistics","Quantum mechanics","Biological cells"
Publisher :
ieee
Conference_Titel :
Natural Computation (ICNC), 2015 11th International Conference on
Electronic_ISBN :
2157-9563
Type :
conf
DOI :
10.1109/ICNC.2015.7377992
Filename :
7377992
Link To Document :
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