本研究将双种群遗传算法引入测试用例排序中以解决单一种群中过早收敛和最终解质量不稳定等问题,通过设置多样性较高的初始解,并在两个进化种群中使用不同的控制参数来协同进化,达到扩大解搜索空间的目的,以降低算法陷入局部最优的风险...本研究将双种群遗传算法引入测试用例排序中以解决单一种群中过早收敛和最终解质量不稳定等问题,通过设置多样性较高的初始解,并在两个进化种群中使用不同的控制参数来协同进化,达到扩大解搜索空间的目的,以降低算法陷入局部最优的风险;同时使用引入权重因子的平均方法覆盖率作为适应度函数,利用Boltzmann选择法实现不同进化阶段选择压力的自适应变化,期望加快算法后期收敛速度。最后在具有真实故障的数据集Defects4J上进行对比验证,结果表明:本文算法在平均故障检测率(average percentage of fault detection,APFD)方面优于单一种群遗传算法,且这种性能的提升在统计学上是显著的。展开更多
The commonly used statistical methods in medical research generally assume patients arise from one homogeneous population. However, the existence and importance of significant heterogeneity have been widely documented...The commonly used statistical methods in medical research generally assume patients arise from one homogeneous population. However, the existence and importance of significant heterogeneity have been widely documented. It is well known that common and complex human diseases usually have heterogeneous disease etiology, which often involves interplay of multiple genetic and environmental factors, leading to latent population substructure. Genome-wide association studies (GWAS) is a useful tool to uncover genetic association with disease of interest, while linkage analysis is a commonly used method to identify statistical association between the inheritance of a human disease and inheritance of marker loci that are in linkage with disease causing loci. We propose a likelihood ratio test for genome-wide linkage analysis under genetic heterogeneity using family data. We derive a closed-form formula for the LRT test statistic and provide explicit asymptotic null distribution. The closed form asymptotic distribution allows easy determination of the asymptotic p-values. Our extensive simulation studies indicate that the proposed test has proper type I error and good power under genetic heterogeneity. In order to simplify application of the proposed method for non-statisticians, we develop an R package gLRTH to implement the proposed LRT for genome-wide linkage analysis as well as Qian and Shao’s LRT for GWAS under heterogeneity. The newly developed open source R package gLRTH is available at CRAN.展开更多
文摘本研究将双种群遗传算法引入测试用例排序中以解决单一种群中过早收敛和最终解质量不稳定等问题,通过设置多样性较高的初始解,并在两个进化种群中使用不同的控制参数来协同进化,达到扩大解搜索空间的目的,以降低算法陷入局部最优的风险;同时使用引入权重因子的平均方法覆盖率作为适应度函数,利用Boltzmann选择法实现不同进化阶段选择压力的自适应变化,期望加快算法后期收敛速度。最后在具有真实故障的数据集Defects4J上进行对比验证,结果表明:本文算法在平均故障检测率(average percentage of fault detection,APFD)方面优于单一种群遗传算法,且这种性能的提升在统计学上是显著的。
文摘The commonly used statistical methods in medical research generally assume patients arise from one homogeneous population. However, the existence and importance of significant heterogeneity have been widely documented. It is well known that common and complex human diseases usually have heterogeneous disease etiology, which often involves interplay of multiple genetic and environmental factors, leading to latent population substructure. Genome-wide association studies (GWAS) is a useful tool to uncover genetic association with disease of interest, while linkage analysis is a commonly used method to identify statistical association between the inheritance of a human disease and inheritance of marker loci that are in linkage with disease causing loci. We propose a likelihood ratio test for genome-wide linkage analysis under genetic heterogeneity using family data. We derive a closed-form formula for the LRT test statistic and provide explicit asymptotic null distribution. The closed form asymptotic distribution allows easy determination of the asymptotic p-values. Our extensive simulation studies indicate that the proposed test has proper type I error and good power under genetic heterogeneity. In order to simplify application of the proposed method for non-statisticians, we develop an R package gLRTH to implement the proposed LRT for genome-wide linkage analysis as well as Qian and Shao’s LRT for GWAS under heterogeneity. The newly developed open source R package gLRTH is available at CRAN.
