计算机工程与应用2016，Vol.52Issue(1)：12-16,5.DOI:10.3778/j.issn.1002-8331.1312-0410
CUDA平台下信息熵多种群遗传算法设计
Information entropy multi-population genetic algorithm based on CUDA
摘要
Abstract
In order to improve the computational efficiency of information entropy multi-population genetic algorithm, and reduce the computing time, an information entropy multi-population genetic algorithm based on CUDA is proposed. By analyzing the parallelism factors of original algorithm, considering the CUDA platform, parallel processing is made on the original algorithm to suit for GPU-accelerated. Genetic operators, penalty function, and space contraction factors are also modified for CUDA parallelism. All these work improve the efficiency of the original algorithm. Under the premise of keeping the fast convergence and accuracy, example numerical tests show that CUDA parallel algorithms has a high acceleration efficiency.关键词
统一计算设备架构(CUDA)/并行计算/遗传算法/信息熵/多种群Key words
Compute Unified Device Architecture(CUDA)/parallel algorithm/genetic algorithm/information entropy/multi-population分类
信息技术与安全科学引用本文复制引用
李正夫,王希诚,李克秋,姚翔,董悦丽..CUDA平台下信息熵多种群遗传算法设计[J].计算机工程与应用,2016,52(1):12-16,5.基金项目
国家自然科学基金(No.61170168,No.61170169). （No.61170168,No.61170169）
