遗传算法及其育种:GA于20世纪60年代由美国Michigan大学J.H.Holland教授[1]首先提出。它可广泛应用于人工智能、机器学习、函数的优化、自动控制等领域。GA的突出特点是将问题的解空间间通过编码转换为GA的搜索空间，把问题的解转换为生物的个体，并借助生物的遗传和进化理论，对多个个体同时进行选择、交叉和变异操作。这样，可以较快地搜索到最优解。但是，遗传算法易陷入局部最优。搜索效率还不是

-Genetic Algorithm and Breeding: GA 1960s first proposed by the University of Michigan, USA JHHolland professor [1]. It can be widely used in artificial intelligence, machine learning, optimization, automatic control functions. The salient features of the GA is the solution space of the problem by transcoding the GA search space, the solution of the problem of biological individuals, and with the help of bio-genetic and evolutionary theory, multiple individual selection, crossover and variation operation. In this way, you can quickly search for the optimal solution. However, the genetic algorithm is easily trapped into local optima. Search efficiency is not