Artificial Immune Privileged Sites as an Enhancement to Immuno-Computing Paradigm

TL;DR

This paper explores how the concept of immune privileged sites from natural immune systems can enhance artificial immune computing paradigms by allowing selective immune responses, potentially improving information processing algorithms.

Contribution

It introduces the novel idea of integrating immune privileged sites into artificial immune systems to enable selective immune responses and adaptive learning.

Findings

Proposes a new immune privilege-based model for artificial immune systems.

Demonstrates potential for improved adaptability and learning.

Suggests applications in complex problem-solving environments.

Abstract

The immune system is a highly parallel and distributed intelligent system which has learning, memory, and associative capabilities. Artificial Immune System is an evolutionary paradigm inspired by the biological aspects of the immune system of mammals. The immune system can inspire to form new algorithms learning from its course of action. The human immune system has motivated scientists and engineers for finding powerful information processing algorithms that has solved complex engineering problems. This work is the result of an attempt to explore a different perspective of the immune system namely the Immune Privileged Site (IPS) which has the ability to make an exception to different parts of the body by not triggering immune response to some of the foreign agent in these parts of the body. While the complete system is secured by an Immune System at certain times it may be required that the system allows certain activities which may be harmful to other system which is useful to it and learns over a period of time through the immune privilege model as done in case of Immune Privilege Sites in Natural Immune System.