A hybrid feature selection and classification strategy was proposed based on the simulated annealing genetic algorithm and multiple instance learning(MIL).The band selection method was proposed from subspace decomposi...A hybrid feature selection and classification strategy was proposed based on the simulated annealing genetic algorithm and multiple instance learning(MIL).The band selection method was proposed from subspace decomposition,which combines the simulated annealing algorithm with the genetic algorithm in choosing different cross-over and mutation probabilities,as well as mutation individuals.Then MIL was combined with image segmentation,clustering and support vector machine algorithms to classify hyperspectral image.The experimental results show that this proposed method can get high classification accuracy of 93.13% at small training samples and the weaknesses of the conventional methods are overcome.展开更多
三维片上网络(3D No C)中IP核的测试问题日趋突出,测试规划是提高测试效率的有效方法。基于重用No C作为测试存取机制的并行测试方法,针对IP核测试数据传输带宽与TAM带宽不匹配的问题,提出带分复用方法,对有限带宽的TAM进行动态细分,将...三维片上网络(3D No C)中IP核的测试问题日趋突出,测试规划是提高测试效率的有效方法。基于重用No C作为测试存取机制的并行测试方法,针对IP核测试数据传输带宽与TAM带宽不匹配的问题,提出带分复用方法,对有限带宽的TAM进行动态细分,将多核的测试数据共享同一物理TAM实施并行传输,并结合3D No C结构设计二维编码,建立带宽分配和测试顺序模型,采用多种群遗传模拟退火算法,在总功耗、层功耗双重约束下对IP核的带宽分配和测试顺序进行双重优化,提高并行测试效率以获得最短测试时间。算法中针对测试顺序优化设计移位互换杂交策略,并运用精英配对方法加快种群寻优速度,设计求精操作进一步优化测试时间,通过比较、淘汰、替换机制加强种群间交流,增加种群多样性,避免算法陷入局部最优。以ITC'02标准电路作为测试对象,实验结果表明,该方法通过提高带宽利用率,提升了并行测试效率,降低了资源占用,有效地缩短了测试时间。展开更多
多种群遗传算法相比遗传算法在性能上能够有所提高,但对具有较多局部最优解的作业车间调度问题,多种群遗传算法仍然难以改善易陷入局部最优解和局部搜索能力差的缺点.因此,提出了一种求解作业车间调度问题的新算法MGA-MBL(multi-populat...多种群遗传算法相比遗传算法在性能上能够有所提高,但对具有较多局部最优解的作业车间调度问题,多种群遗传算法仍然难以改善易陷入局部最优解和局部搜索能力差的缺点.因此,提出了一种求解作业车间调度问题的新算法MGA-MBL(multi-population genetic algorithm based on memory-base and Lamarckian evolution for jobshop scheduling problem).MGA-MBL在多种群遗传算法的基础上通过引入记忆库策略,不但使子种群间的个体可以进行信息交换,而且有利于保持整个种群的多样性;通过构造基于拉马克进化机制的局部搜索算子来提高多种群遗传算法中子种群进化的局部搜索能力.由于MGA-MBL采用了全局寻优能力较强的模拟退火算法对记忆库中的个体进行优化,从而缓解了多种群遗传算法易陷入局部最优解的问题,并提高了算法求解作业车间调度问题的性能.对著名的benchmark数据进行测试,实验结果证实了MGA-MBL在求解作业车间调度问题上的有效性.展开更多
文摘A hybrid feature selection and classification strategy was proposed based on the simulated annealing genetic algorithm and multiple instance learning(MIL).The band selection method was proposed from subspace decomposition,which combines the simulated annealing algorithm with the genetic algorithm in choosing different cross-over and mutation probabilities,as well as mutation individuals.Then MIL was combined with image segmentation,clustering and support vector machine algorithms to classify hyperspectral image.The experimental results show that this proposed method can get high classification accuracy of 93.13% at small training samples and the weaknesses of the conventional methods are overcome.
文摘三维片上网络(3D No C)中IP核的测试问题日趋突出,测试规划是提高测试效率的有效方法。基于重用No C作为测试存取机制的并行测试方法,针对IP核测试数据传输带宽与TAM带宽不匹配的问题,提出带分复用方法,对有限带宽的TAM进行动态细分,将多核的测试数据共享同一物理TAM实施并行传输,并结合3D No C结构设计二维编码,建立带宽分配和测试顺序模型,采用多种群遗传模拟退火算法,在总功耗、层功耗双重约束下对IP核的带宽分配和测试顺序进行双重优化,提高并行测试效率以获得最短测试时间。算法中针对测试顺序优化设计移位互换杂交策略,并运用精英配对方法加快种群寻优速度,设计求精操作进一步优化测试时间,通过比较、淘汰、替换机制加强种群间交流,增加种群多样性,避免算法陷入局部最优。以ITC'02标准电路作为测试对象,实验结果表明,该方法通过提高带宽利用率,提升了并行测试效率,降低了资源占用,有效地缩短了测试时间。
文摘多种群遗传算法相比遗传算法在性能上能够有所提高,但对具有较多局部最优解的作业车间调度问题,多种群遗传算法仍然难以改善易陷入局部最优解和局部搜索能力差的缺点.因此,提出了一种求解作业车间调度问题的新算法MGA-MBL(multi-population genetic algorithm based on memory-base and Lamarckian evolution for jobshop scheduling problem).MGA-MBL在多种群遗传算法的基础上通过引入记忆库策略,不但使子种群间的个体可以进行信息交换,而且有利于保持整个种群的多样性;通过构造基于拉马克进化机制的局部搜索算子来提高多种群遗传算法中子种群进化的局部搜索能力.由于MGA-MBL采用了全局寻优能力较强的模拟退火算法对记忆库中的个体进行优化,从而缓解了多种群遗传算法易陷入局部最优解的问题,并提高了算法求解作业车间调度问题的性能.对著名的benchmark数据进行测试,实验结果证实了MGA-MBL在求解作业车间调度问题上的有效性.
