An Improved Many Worlds Quantum Genetic Algorithm

被引：0

作者：

Li, Dan ^{[1
]}

Zhao, Junsuo ^{[1
]}

Zhang, Heng

Qiao, Peng

Zhuang, Jiayu ^{[2
,3
]}

机构：

[1] Chinese Acad Sci, Inst Software, Sci & Technol Integrated Informat Syst Lab, Beijing 100190, Peoples R China

[2] Chinese Acad Agr Sci, Agr Informat Inst, Beijing 100081, Peoples R China

[3] Minist Agr, Key Lab Agri Informat Serv Technol, Beijing 100081, Peoples R China

来源：

2015 11TH INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION (ICNC) | 2015年

关键词：

Quantum Genetic Algorithm; Many Worlds; quantum training operator; combinatorial optimization operator;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

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.

引用

页码：210 / 214

页数：5

共 8 条

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