A Novel Negative Selection Algorithm with Optimal Worst-case Training Time Complexity for R-chunk Detectors

Nguyen Van Truong

doi:10.17485/ijst/2020/v13i10/149803

Abstract

Objectives: To generate complete and non-redundant detector set with optimal worst-case time complexity. Methods: In this study, a novel exact matching and string-based Negative Selection Algorithm utilizing r-chunk detectors is proposed. Improved algorithms are tested on some data sets; the experiments’ results are compared with recently published ones. Moreover, algorithms’ complexities are also proved mathematically. Findings: For string-based Artificial Immune Systems, r-chunk detector is the most common detector type and their generation complexity is one of the important factors considered in the literature. We proposed optimal algorithms based on automata to present all detectors. Novelty/applications: The algorithm could generate the representation of complete and nonredundant detector set with optimal worst-case time complexity. To the best of our knowledge, the algorithm is the first one to possess such worst-case training time complexity. Keywords: Artificial Immune Systems, Negative Selection Algorithms, Positive Selection Algorithms, Detector Sets, Self, Non-self.

Highlights

The biological immune system is a cooperative system that provides a comprehensive line of defense for human against pathogens
The selection process acts on the newborn T cells to ensure that they could only recognize non-self not self
A randomly created dataset containing 50,000 strings of length 100 is used for experiment 2

Summary

Introduction

The biological immune system is a cooperative system that provides a comprehensive line of defense for human against pathogens. After million years of evolution, it has become a defensive system that is adaptive, inherently distributed, and incredibly robust. It possesses powerful capabilities such as pattern recognition, learning, and memory which helps to combat infections caused by pathogens (such as viruses), even though it needs no central control or coordination. The main player in the biological immune system is the T cells, which could recognize selves and contain an antigen receptor for locating and binding to infected pathogens (non-selves). For detecting non-selves, the biological immune system conducts its learning process in two steps, which does not require any negative example. The selection process acts on the newborn T cells to ensure that they could only recognize non-self not self (to avoid autoimmune reactions). Algorithms that are abstracted and inspired from this selection process are named Negative Selection Algorithms (NSAs)

Objectives

Methods

Results

Conclusion