In general, the initial population

In general, the initial population is very important for any MH method since it affects the convergence of an algorithm and the quality of the final solutions. According to the characteristics of the OBL strategy, it can be used to construct the initial population which accelerates the convergence rate. <br>For example, if we consider the values ​​of xi are [0.93, 0.33, 0.61, 0.54, 0.28, 0.34, 0.86], then the output of Eq. (11) is represented as [1, 0, 1, 0 , 0, 0, 1]. This representation means that the features in the dataset which are corresponding to ones are selected as relevant features and which are corresponding to 0's will be ignored.

In general, the initial is very big for any MH method since it it the convergence of an algorithm and the quality of the final solutions. A to the ters of the oBL strategy, it can be edlet to construct the initial population what's the accelerates the convergence rate.<br>For example, if consider we the values of the values of the ones are (0.93, 0.33, 0.61, 0.54, 0.28, 0.34, 0.86), then the output of Eq. (11) is represented as 1, 0, 1, 0, 0, 0, 0, 1. This is the representation of the means the features in the dataset who are to one s are selected as the first features and who are to be 0's will be ignored.

一般来说，初始种群对任何MH方法都非常重要，因为它影响算法的收敛性和最终解的质量。根据OBL策略的特点，可以利用它构造初始种群，加快收敛速度。<br>例如，如果我们考虑席的值是[ 0.93，0.33，0.61，0.54，0.28，0.34，0.86 ]，那么等式（11）的输出表示为[1, 0, 1，0, 0, 0，1 ]。这种表示方式意味着数据集中与一个特征相对应的特征被选择为相关特征，而与0对应的特征将被忽略。<br>