Memristor Parallel Computing for a Matrix-Friendly Genetic Algorithm
提出一种矩阵友好的遗传算法，用矩阵运算实现种群进化，在忆阻器硬件上并行部署，比传统遗传算法快2.5倍，并在声纳数据集特征选择中减少46个特征、提升11.9%准确率。
Matrix operation is easy to be paralleled by hardware, and the memristor network can realize a parallel matrix computing model with in-memory computing. This article proposes a matrix-friendly genetic algorithm (MGA), in which the population is represented by a matrix and the evolution of population is realized by matrix operations. Compared with the performance of a baseline genetic algorithm (GA) on solving the maximum value of the binary function, MGA can converge better and faster. In addition, MGA is more efficient because of its parallelism on matrix operations, and MGA runs 2.5 times faster than the baseline GA when using the NumPy library. Considering the advantages of the memristor in matrix operations, memristor circuits are designed for the deployment of MGA. This deployment method realizes the parallelization and in-memory computing (memristor is both memory and computing unit) of MGA. In order to verify the effectiveness of this deployment, a feature selection experiment of logistic regression (LR) on Sonar datasets is completed. LR with MGA-based feature selection uses 46 fewer features and achieves 11.9% higher accuracy.