Optimization of self set and detector generation base on Real-value negative selection algorithm

Abstract

The Artificial Immune System (AIS) community has been vibrant and active for a number of years now. Artificial Immune Systems (AIS) are a type of intelligent algorithm inspired by the principles and processes of the human immune system. Applications of AIS have been studied in various fields. In the application of anomaly detection, negative selection algorithms of AIS have been successfully applied. Real-valued Negative selection algorithms generate their detector sets based on the points of self data. This paper mainly focuses on self set existing problems and solutions. definite the detector radius according to self radius, and propose negative selection algorithm which is decided by detector radius according to self radius, this way of improved RNS may avoid the detector boundary cross problem. Experiments show that the effect of self region optimized is prominent, and performances of detectors is highly efficient.