将下面的英文翻译成中文6

来源：百度知道 编辑：UC知道 时间：2024/06/02 08:29:38
Unlike traditional optimisation methods, GA is better at handling integer variables than continuous variables. This is due to the inherent granularity of variable gene strings within the GA model structure. Typically, a variable is implemented with a range of possible values with a binary string indicating the number of such values; i.e. if x1[0,15] and the gene string is 4 characters (e.g. “1010”) then there are 16 possibilities for the search to consider. To model this as a continuous variable increases the number of possible values significantly. Similarly, other variable information which aids the search considerably are upper and lower bound values. These factors can affect convergence of the model solutions greatly.

与传统的优化方法，遗传算法是更好地处理整数变量比连续变量。这是由于固有的粒度可变基因字符串内的遗传模型结构。通常情况下，一个变量是实施了一系列可能的值与一个二进制字符串，表明一些诸如价值观;即如果x1  [ 0,15 ]和基因字符串是4个字符（例如“ 1010 ” ） ，则有16个的可能性搜索审议。模型这是一个连续变量增加了一些可能的值显着。同样，其他变量信息艾滋病搜索大大的上限和下限的价值观。这些因素可能会影响收敛的模型解决方案极大。