Fuzzy Genetic Sharing for Dynamic Optimization

作     者：Khalid Jebari Abdelaziz Bouroumi Aziz Ettouhami 

作者机构：Conception and Systems LaboratoryFaculty of SciencesMohammed V-Agdal University Modeling and Instrumentation LaboratoryBen Msik Faculty of SciencesHassan II Mohammedia-Casablanca University 

出 版 物：《International Journal of Automation and computing》 (国际自动化与计算杂志（英文版）)

年 卷 期：2012年第9卷第6期

页      面：616-626页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Genetic algorithms unsupervised learning fuzzy clustering dynamic optimization evolutionary algorithms dynamic niche sharing Hill s diversity index multi-modal function optimization. 

摘      要：Recently,genetic algorithms(GAs) have been applied to multi-modal dynamic optimization(MDO).In this kind of optimization,an algorithm is required not only to find the multiple optimal solutions but also to locate a dynamically changing *** fuzzy genetic sharing(FGS) approach is based on a novel genetic algorithm with dynamic niche sharing(GADNS).FGS finds the optimal solutions,while maintaining the diversity of the *** this,FGS uses several ***,an unsupervised fuzzy clustering method is used to track multiple optima and perform ***,a modified tournament selection is used to control selection ***,a novel mutation with an adaptive mutation rate is used to locate unexplored search *** effectiveness of FGS in dynamic environments is demonstrated using the generalized dynamic benchmark generator(GDBG).