中文摘要：

瞄准了在增加的药设计有效地解决连续最佳参数问题，这份报纸开发一个新奇蚂蚁算法称为的连续 gridded 蚂蚁殖民地(CGAC ) ，在间谍蚂蚁被利用完全并且有效地在领域寻找潜伏的最佳格子的地方。以便测试效果， CGAC 算法是在发现 C 的最好的价值的成功并且，当支持向量机器(SVM ) 被用来适合在化学药品的数字表示之间的非线性的关系时，组织并且 IC50。基因算法(GA ) 也被用来同时获得适当特征子集，因为特征子集选择影响适当内核参数并且反过来也如此。获得的结果说明模型有的那 GA-CGAC-SVM 令人满意的预言精确性。在 13 描述符的模型的最好的量的建模结果基于有吝啬平方的错误的 GA-CGAC-SVMr 0.397，一个预言的关联系数(R2 ) 0.842，并且一个跨 validated 关联系数(Q2 ) 0.756。最好的分类结果用 SVM 被发现：正确预言的百分比(%) 基于 7 褶层交叉验证是 90.6% 。结果证明建议 CGAC 算法提供一个新、有效的方法当 SVM 工具被使用时，发现最佳参数。

英文摘要：

Aimed at solving continuous optimum parameter problems effectively in added drug design, this paper develops a novel ant algorithm termed continuous gridded ant colony （CGAC）, where the spy ants are utilized to search the latent optimum grid in the domain completely and effectively. In order to test the effect, the CGAC algorithm was success in finding the best values of C and y, when the support vector machine （SVM） was used to fit the nonlinear relationship between the numerical representation of the chemical structure and IC50. The genetic algorithm （GA） was also used to obtain the appropriate feature subset simultaneously, because feature subset selection influences the appropriate kernel parameters and vice versa. The obtained results illustrate that GA-CGAC-SVM models have satisfactory prediction accuracy. The best quantitative modeling results in thirteen-descriptors model based on GA-CGAC-SVMr with mean-square errors 0.397, a predicted correlation coefficient （R2） 0.842, and a cross-validated correlation coefficient （Q^2） 0.756. The best classification result was found using SVM： the percentage （%） of correct prediction based on 7-fold cross-validation was 90.6%. The results demonstrate that the proposed CGAC algorithm provides a new and effective method to find the optimum parameters when the SVM tool is used.