Gene sorting in differential evolution withcross-generation mutation

作     者：TASSING Remi 

作者机构：Department of Electronics and Information Engineering Huazhong University of Science and Technology 

出 版 物：《Science China(Information Sciences)》 (中国科学:信息科学(英文版))

年 卷 期：2011年第54卷第2期

页      面：268-278页

核心收录：

学科分类：0710[理学-生物学] 0810[工学-信息与通信工程] 0808[工学-电气工程] 07[理学] 071007[理学-遗传学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the International Science and Technology Cooperation Programme of China (Grant Nos.2008DFA11630,2008AA01Z204) the National Natural Science Foundation of China (Grand Nos.60496315,60802009) Hubei Science Foundation (Grant No.2007ABA008) Postdoctoral Foundation (Grant No.20070410279) 

主　　题：differential evolution evolutionary algorithm genetic algorithm gene sorting cross-generation mutation 

摘      要：Gene sorting is proposed in this paper as a method of ordering trial vector’s component in dif- ferential evolution (DE). This method tends to significantly increase the convergence speed of DE with just a little modification on the original algorithm. In the meantime, a new concept of cross-generation mutation is introduced in order to perform the evolution process serially rather than parallelly. When combined with gene sorting, this method will further increase the convergence speed. A benchmark set of 18 functions is used to investigate the performance of these algorithms. Most importantly, the proposed methods can be incorporated in other variants of DE to further increase their respective speeds. Three versions of self-adaptive DE, namely iterated function system based adaptive differential evolution (IFDE), Janez’s DE (jDE) and SaDE, are taken as examples, which are averagely 10, 6 and 4 times faster than the benchmark set respectively.