Dendritic Cell Algorithm with Grouping Genetic Algorithm for Input Signal Generation

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