改进GA算法结合ANN用于酚类化合物的QSAR研究  

QSAR for toxicities of phenols using improved genetic algorithm combined with BP artificial neural network

作　　者：杨蕾[1] 王鹏[1] 蒋益林[1] 夏冰[1] 

机构地区：[1]哈尔滨工业大学应用化学系

出　　处：《哈尔滨工业大学学报》2006年第2期216-218,共3页Journal of Harbin Institute of Technology

基　　金：黑龙江省自然科学基金资助项目(B200512);哈尔滨工业大学校基金资助项目(2002-58)

摘　　要：采用改进的遗传算法(IGA)和BP人工神经网络相结合的方法,研究了50个酚类化合物的麻醉毒性和分子结构之间的相关性,并与单纯用BP人工神经网络建立的模型进行比较.结果表明,该方法克服了人工神经网络训练中的局部最优问题,采用最优交叉和变异等遗传策略,有效地解决了收敛过程中的振荡问题,所得模型的训练精度和预测精度均优于单纯的BP人工神经网络QSAR模型.Using an improved genetic algorithm combined with a BP algorithm a new method IGA - BP, QSAR model was developed that links molecular structures of a set of 50 alkylated and/or halogenated phenols with their polar narcosis toxicity. Comparing the results of IGA - BP with those of BP algorithm demonstrated that the method of IGA - BP overcame a local optimal solution which often occurs in the training process of artificial neural network. ＂Genetic strategies, such as optimal crossover and optimal mutation, which can efficiently extinguish the swing problem in a convergent process, were employed that resulted in lower errors than those of the BP algorithm.

关 键 词：定量构效关系 遗传算法 BP-人工神经网络 酚类化合物 

分 类 号：X171.5[环境科学与工程—环境科学]