量子概率编码遗传算法及其应用

该文提出了一种基于染色体量子概率编码的遗传算法--QCGA。与传统遗传算法不同,在QCGA中, 单个个体不再表示某一个确定解,而是解的取值概率分布,覆盖整个解空间;各个个体独立并行演化,个体间通过一个新的交叉算子实现演化信息的交换,同时设计了一个新的变异算子以增强算法的局部寻优能力。为了充分考察该算法的有效性和先进性,将其应用于典型函数优化、0-1背包问题和时间序列中频繁结构模式搜索等问题的求解。实验结果表明,与现有同类算法相比,该算法在具有很高搜索效率的同时,仍能维持很高的种群多样性, 因而适用于复杂优化问题的求解。

Quantum Probability Coding Genetic Algorithm and Its Applications

A Quantum probability Coding Genetic Algorithm-QCGA is proposed, which is different from classical GAs. In QCGA, single individual represents a probability distribution of solutions, which covers the whole solution space. Individuals in QCGA evolve independently and in parallel. A new crossover operator is designed to implement the information exchange among individuals. A new mutation operator is also design to prevent the algorithm from falling into local optima. To study the efficiency and advantage of QCGA, the algorithm is applied to solve function optimization problems, knapsack problems, and to discover frequent structures from time series. Experimental results show that QCGA has good ability of global optimization, and good ability of diversity reservation, which makes it efficient for complex optimization problems.