基于改进集成学习分类的代理辅助进化算法

当使用代理辅助进化算法求解昂贵高维多目标优化问题时,代理模型通常用于近似昂贵的适应度函数.然而,随着目标数的增加,近似误差将逐渐累积,计算量也会急剧增加.对此,提出一种基于改进集成学习分类的代理辅助进化算法,使用一种改进的装袋集成学习分类器作为代理模型.首先,从被昂贵的适应度评价的个体中选择一组分类边界,将所有个体分成两类;其次,利用这些带有分类标签的个体训练分类器,以对候选个体的类别进行预测;最后,选择有前途的个体进行昂贵适应度评价.实验结果表明,算法中所提出的代理模型可有效提高基于分类的代理辅助进化算法求解昂贵高维多目标优化问题的能力,且与目前流行的代理辅助进化算法相比,基于改进集成学习分类的代理辅助进化算法更具竞争力.

Improved ensemble learning classification based surrogate-assisted evolutionary algorithm

When using surrogate-assisted evolutionary algorithm to solve the expensive many-objective optimization problems, the surrogate is usually used to approximate the expensive fitness function. However, with the increase of the number of objectives, the approximation error will accumulate gradually and the amount of calculation will increase sharply. In order to solve this problem, we propose an improved ensemble learning classification based surrogate-assisted evolutionary algorithm, which uses an improved bagging ensemble as the surrogate. Firstly, a set of classification boundary individuals are selected from the individuals evaluated by the expensive fitness function, and the individuals are divided into two groups. Then, these individuals with the group labels are used to train a classifier to predict the groups of the candidate individuals. Finally, the promising individuals are selected to be evaluated by the expensive fitness function. The experimental results show that the proposed surrogate in the algorithm effectively improves the ability of the classification based surrogate-assisted evolutionary algorithm to solve the expensive many-objective optimization problems, and compared with the current popular surrogate-assisted evolutionary algorithms, the proposed improved ensemble learning classification based surrogate-assisted evolutionary algorithm is more competitive.