基于多层次信息反馈的混合蛙跳算法(Shuffled Frog Leaping Algoriyhm based on the interation of Multi-level information, MSFLA),吸收遗传算法的交叉算子及粒子群算法(PSO)的粒子进化方式,将整个优化过程划分为标准混合蛙跳优化层...基于多层次信息反馈的混合蛙跳算法(Shuffled Frog Leaping Algoriyhm based on the interation of Multi-level information, MSFLA),吸收遗传算法的交叉算子及粒子群算法(PSO)的粒子进化方式,将整个优化过程划分为标准混合蛙跳优化层、青蛙进化与学习层、外部档案信息交换层。混合蛙跳优化层保证青蛙进行正常的局部搜索优化(蛙跳算法);青蛙进化与学习层保证青蛙每次迭代结束时都能得到更好的自身位置(PSO粒子进化方式);外部档案信息交换层可以保证青蛙种群获得最优解(交叉算子)。通过各层次之间的信息交流,提高算法的性能。从实验结果对比能够得出,改进后的MSFLA算法可以有效地改善早熟收敛问题,具有更好的收敛速度和更高的寻优精度。展开更多
The relative toxicity of 48 anilines using the Tetrahymena pyriformis population growth characteristics IGC50 （concentration causing 50% growth inhibition）, available in the literature, was studied. At first, the en...The relative toxicity of 48 anilines using the Tetrahymena pyriformis population growth characteristics IGC50 （concentration causing 50% growth inhibition）, available in the literature, was studied. At first, the entire data set was randomly split into a training set （31 chemicals） used to establish the QSAR model, and a test set （17 chemicals） for statistical external validation. A biparametric model was developed using, as independent variables, 3D theoretical descriptors derived from DRAGON software. The GA-MLR （genetic algorithm variable subset selection） procedure was performed on the trainingset by the software mobydigs using the OLS （ordinary least squares） regression method, and GA（genetic algorithm）-VSS（variable subset selection） by maximising the cross-validated explained variance （Q^2Loo）＇ The obtained model was examined for robustness （Q^2LOOcross-validation, Y-scrambling） and predictive ability through both internal （Q^2LM0, bootstrap） and external validation （Q^2ext） methods. Descriptors included in the QSAR model indicated that log/GC^-150 value was related to molecular size and shape, and interaction of molecule with its surrounding medium or its target. Moreover, the applicability domain of the model was discussed.展开更多
文摘基于多层次信息反馈的混合蛙跳算法(Shuffled Frog Leaping Algoriyhm based on the interation of Multi-level information, MSFLA),吸收遗传算法的交叉算子及粒子群算法(PSO)的粒子进化方式,将整个优化过程划分为标准混合蛙跳优化层、青蛙进化与学习层、外部档案信息交换层。混合蛙跳优化层保证青蛙进行正常的局部搜索优化(蛙跳算法);青蛙进化与学习层保证青蛙每次迭代结束时都能得到更好的自身位置(PSO粒子进化方式);外部档案信息交换层可以保证青蛙种群获得最优解(交叉算子)。通过各层次之间的信息交流,提高算法的性能。从实验结果对比能够得出,改进后的MSFLA算法可以有效地改善早熟收敛问题,具有更好的收敛速度和更高的寻优精度。
文摘The relative toxicity of 48 anilines using the Tetrahymena pyriformis population growth characteristics IGC50 （concentration causing 50% growth inhibition）, available in the literature, was studied. At first, the entire data set was randomly split into a training set （31 chemicals） used to establish the QSAR model, and a test set （17 chemicals） for statistical external validation. A biparametric model was developed using, as independent variables, 3D theoretical descriptors derived from DRAGON software. The GA-MLR （genetic algorithm variable subset selection） procedure was performed on the trainingset by the software mobydigs using the OLS （ordinary least squares） regression method, and GA（genetic algorithm）-VSS（variable subset selection） by maximising the cross-validated explained variance （Q^2Loo）＇ The obtained model was examined for robustness （Q^2LOOcross-validation, Y-scrambling） and predictive ability through both internal （Q^2LM0, bootstrap） and external validation （Q^2ext） methods. Descriptors included in the QSAR model indicated that log/GC^-150 value was related to molecular size and shape, and interaction of molecule with its surrounding medium or its target. Moreover, the applicability domain of the model was discussed.
