计算机科学与探索Issue(4)：397-405,9.DOI:10.3778/j.issn.1673-9418.1308011
动态种群划分量子遗传算法求解几何约束
Geometric Constraint Solving Based on Dynamic Population Divided Quantum Genetic Algorithm
摘要
Abstract
The constraint equations of geometric constraint problem can be transformed into the optimization model, therefore constraint solving problem can be transformed into the optimization problem. Lack of information exchange between the individuals, the traditional quantum genetic algorithm is easy to fall into a local optimum. This paper proposes a dynamic population divided quantum genetic algorithm (DPDQGA) which is applied to geometric constraint solving. The individuals in populations exchange information spontaneously according to certain rules. In the beginning stage of the evolution of each generation, the individual fitness of two initial populations is calculated respectively. After merging the two populations, the league selection method is used to score the individuals in populations, and the populations are ranked according to the score. Finally, the merged populations are re-divided into two sub-populations. The experiments show that DPDQGA for solving geometric constraint problems has better accuracy and solving rate.关键词
几何约束求解/量子遗传算法/动态种群划分Key words
geometric constraint solving/quantum genetic algorithm/dynamic population divide分类
信息技术与安全科学引用本文复制引用
曹春红,王鹏..动态种群划分量子遗传算法求解几何约束[J].计算机科学与探索,2014,(4):397-405,9.基金项目
The National Natural Science Foundation of China under Grant No.61300096(国家自然科学基金) （国家自然科学基金）
the Postdoctoral Science Foun-dation of China under Grant No.2012M520640(中国博士后科学基金) （中国博士后科学基金）
