摘要
量子遗传算法建立在量子的态矢量表达基础上,染色体的编码用量子比特的几率幅表示,使得一条染色体表达多个态的叠加,再利用量子门实现染色体更新操作,从而达到目标的优化求解。它具有种群规模小而不影响算法性能,收敛速度快和全局优化能力强等特点。但是遗传算法的随机性不好把握,收敛方向不好控制,针对遗传算法的种种问题,通过多种方法来对收敛性进行研究。
Quantum genetic algorithm for quantum state vector for the expression, the probability amplitude of quantum bits express chromosome coding, a chromosome can express multiple state. Then update chromosome by quantum gates, to gain objective optimal solution. It has a smal.1 population size without affecting algorithm performance, fast convergence and global optimization ability and other characteristics. However, the randomness of genetic algorithms good grasp, poor control of the direction of convergence. Genetic algorithms for the problems, this paper through a variety of ways carries out a study on the convergence.
出处
《信息技术》
2013年第10期161-164,共4页
Information Technology
关键词
量子遗传算法
收敛性
背包问题
量子旋转门
quantum genetic algorithm
convergence
knapsack problem
quantum gate
