Simulation of Adaptive Clonal Selection Principle in Artificial Immune Systems

In this study, we implemented a simulation of the adaptive clonal selection principle in artificial immune systems using MATLAB. This functionality enables the system to autonomously adjust and optimize its clonal selection process in response to environmental changes. The implementation utilizes MATLAB's computational capabilities to model immune response mechanisms through algorithmic representations of antigen recognition, antibody cloning, and affinity maturation processes. Through this simulation, we can effectively investigate and understand the adaptive capabilities and performance of artificial immune systems across various scenarios. Furthermore, we conducted comprehensive analysis and discussion of the simulation results, incorporating performance metrics and optimization evaluations to thoroughly assess the system's potential and operational characteristics. Key MATLAB functions employed include population initialization routines, fitness calculation algorithms, and mutation operators that collectively simulate the biological clonal selection mechanism.