Dendritic Cell Algorithm with Grouping Genetic Algorithm for Input Signal Generation

作     者：Dan Zhang Yiwen Liang Hongbin Dong 

作者机构：School of Computer ScienceWuhan UniversityWuhan430072China School of Cyber Science and EngineeringWuhan UniversityWuhan430072China 

出 版 物：《Computer Modeling in Engineering & Sciences》 (工程与科学中的计算机建模（英文）)

年 卷 期：2023年第135卷第6期

页      面：2025-2045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）] 

基　　金：NSFC http://www.nsfc.gov.cn/for the support through Grants No.61877045 Fundamental Research Project of Shenzhen Science and Technology Program for the support through Grants No.JCYJ2016042815-3956266 

主　　题：Dendritic cell algorithm combinatorial optimization grouping problems grouping genetic algorithm 

摘      要：The artificial immune system,an excellent prototype for developingMachine Learning,is inspired by the function of the powerful natural immune *** one of the prevalent classifiers,the Dendritic Cell Algorithm(DCA)has been widely used to solve binary problems in the real *** classification of DCA depends on a data preprocessing procedure to generate input signals,where feature selection and signal categorization are themain ***,the results of these studies also show that the signal generation of DCA is relatively weak,and all of them utilized a filter strategy to remove unimportant *** filtered features and applying expertise may not produce an optimal classification *** overcome these limitations,this study models feature selection and signal categorization into feature grouping *** study hybridizes Grouping Genetic Algorithm(GGA)with DCA to propose a novel DCA version,GGA-DCA,for accomplishing feature selection and signal categorization in a search *** GGA-DCA aims to search for the optimal feature grouping scheme without expertise *** this study,the data coding and operators of GGA are redefined for grouping *** experimental results show that the proposed algorithm has significant advantages over the compared DCA expansion algorithms in terms of signal generation.