We use a genetic algorithm to construct Hadamard Matrices. The initial population of random matrices is generated to have a balanced number of +1 and -1 entries in each column except the first column with all +1. Several fitness functions are implemented in order to find the most effective one. The crossover process creates offspring matrix population by exchanging columns of the parent matrix population. The mutation process flips +1 and -1 entry pairs in random columns. The use of CuPy library in Python on graphics processing units enables us to handle populations of thousands of matrices and matrix operations in parallel.
翻译：我们使用遗传算法来构建哈达马德矩阵。 随机矩阵的初始数量生成时, 每一列的条目数量均匀为+1和-1, 除了第一列和所有+1之外, 每个列的条目数量均匀。 为了找到最有效的功能, 实施了几种健身功能。 交叉过程通过交换母矩阵群的列来创建后代矩阵群。 突变过程在随机列中翻转+1和-1。 在平通的 CuPy图书馆用于图形处理器, 使我们能够平行处理成千上万个矩阵和矩阵操作。
相关内容
微信扫码咨询专知VIP会员