摘要
演化算法中,预选择算子用于为后续的环境选择过程筛选出好的潜在候选后代解.现有预选择算子大多基于适应值评估、代理模型或分类模型.由于预选择过程本质上是一个分类过程,因此基于分类的预选择过程天然适用于演化算法.先前研究工作采用二分类或多分类模型进行预选择,需预先准备“好”和“差”两组或具有区分性的多组训练样本来构建分类模型,而随着演化算法的执行,“好”解和“差”解之间的界限将愈加模糊,因此准备具有区分性的两组或多组训练样本将变得具有挑战性.为解决该问题,本文提出了一种基于单分类的预选择策略(One-class Classification based PreSelection,OCPS),首先将当前种群中的解均视为“好”类样本,之后只利用该类“好”样本构建单分类模型,然后利用构建的模型对产生的多个候选解进行标记与选择.提出的策略应用在三个代表性演化算法中,数值实验结果表明,提出的策略能够提升现有演化算法的收敛速度.
In evolutionary algorithms,a preselection operator is a part of the reproduction procedure that aims to choose some promising candidate offspring solutions for further environmental selection.The preselection operator can help to improve the algorithm performance significantly if it is correctly utilized.In preselection,the unpromising candidate solutions can be discarded before the real function evaluations,and thus,the computational resources can be saved.Also,using the preselection operator in evolutionary algorithms will help the algorithms to generate more potentially good solutions in one generation without adding more function evaluations.Most existing preselection operators are based on fitness evaluations,surrogate models,and classification models.Since a preselection operator can be regarded as a classification procedure,where selected solutions can be treated as‘positive’ones and the discarded solutions are‘negative’ones.In terms of this situation,using the classification model to assist preselection is a natural choice for evolutionary algorithms.Previous research uses binary and/or multi-class classification models to guide preselection,in which‘positive’and‘negative’training samples or more classes of samples should be prepared to build the classification model.However,after several generations,for some of the evolutionary algorithms,almost all of the solutions in the current population are relatively‘positive’ones.Thus,the gap between‘positive’and‘negative’solutions is not easy to be defined.Furthermore,for these kinds of evolutionary algorithms,preparing‘negative’training samples has three disadvantages:(1)to reduce the accuracy of the classification model on prediction,(2)to improve the cost on model building,(3)to increase the complexity of algorithms.For this reason,it is not trivial to prepare‘positive’and‘negative’training samples.To deal with this problem and avoid the above disadvantages,we consider employing the one-class classification(OCC)model,whi
作者
张晋媛
周爱民
张桂戌
ZHANG Jin-Yuan;ZHOU Ai-Min;ZHANG Gui-Xu(School of Computer Science and Technology,East China Normal University,Shanghai 200062)
出处
《计算机学报》
EI
CSCD
北大核心
2020年第2期233-249,共17页
Chinese Journal of Computers
基金
国家自然科学基金(61731009,61673180,61703382)资助.
关键词
全局优化
演化算法
分类
单分类
预选择
global optimization
evolutionary algorithm
classification
one-class classification
preselection
