量子克隆遗传算法
Quantum Clonal Genetic Algorithms
遗传算法是解决优化问题的一种有效方法。但在实际应用中也存在着收敛速度慢，早熟等问题，使得其结果极不稳定。本文将遗传算法和量子理论相结合并利用免疫系统中所特有的克隆算子，针对0/1背包问题，提出了一种改进的进化算法——量子克隆遗传算法(QCA)。它能有效的避免早熟，且具有收敛速度快的特点。
Genetic algorithm is an effective algorithm in solving the optimizing problem, but it has some disadvantages in the application, such as slow converging speed and prematurity. In this paper, an improved evolutionary algorithm, called the quantum clonal genetic algorithms (QCA), is proposed based on the combining of quantum theory with genetic theory and with the main mechanisms of clone. QCA can availably solve 0/1 knapsack problem and it has better diversity and the converging speed than the classical genetic algorithms.
焦李成、李阳阳
计算技术、计算机技术
遗传算法 量子克隆遗传算法 0/1背包
Genetic algorithm Quantum Clonal Genetic Algorithm 0/1 knapsack
焦李成,李阳阳.量子克隆遗传算法[EB/OL].(2006-11-06)[2025-08-02].http://www.paper.edu.cn/releasepaper/content/200611-100.点此复制
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