Fundamental Theoretical Applications and Core Algorithms of Artificial Immune Systems
Simulation and Implementation of Basic Theories and Core Algorithms in Artificial Immune Systems
Simulation of Adaptive Clonal Selection Principle in Artificial Immune Systems
MATLAB-based implementation simulating the adaptive clonal selection principle functionality in artificial immune systems, featuring algorithm optimization and performance analysis
MATLAB Algorithm for Artificial Immune Systems
Source code implementation of MATLAB algorithms for artificial immune systems, widely used in artificial intelligence with significant educational value for studying immune-inspired computing techniques.
Artificial Immune System Toolbox
Artificial Immune System toolbox for MATLAB with comprehensive immune algorithm implementations
Artificial Immune System Algorithm Source Code
A well-debugged source code implementation of an artificial immune system algorithm, ready for deployment with comprehensive functionality.
Negative Selection Algorithm Implementation in Artificial Immune Systems
This programming example demonstrates the Negative Selection Algorithm from artificial immune systems, primarily used for anomaly detection. The implementation includes Python code examples and practical testing methodologies.
Artificial Immune System Toolbox for MATLAB
A comprehensive toolbox for artificial immune systems, specifically designed for implementation and research in MATLAB environment.
Artificial Immune System
Algorithm Process: 1. Parameter Configuration Random generation of initial population - population = initpop(popsize, chromlength) Fault type encoding, each row represents: code(1,:) for normal; code(2,:) for 50%; code(3,:) for 1.5%. Actual fault measurement data encoding, referred to as Unnormalcode, 188% 4. Iteration Initialization (M): 1) Calculate objective function value: Euclidean distance [objvalue] = calobjvalue(population, i) 2) Calculate fitness value for each individual in population: fitvalue = calfitvalue(objvalue)
Analysis of Artificial Immune Systems with Network Intrusion Detection Implementation
This study analyzes the principles of artificial immune systems and implements a network intrusion detection system using the negative selection algorithm. The system incorporates an r-chunk matching rule for anomaly event recognition, with vulnerability analysis conducted on the rule-set to enhance detection accuracy and security robustness.
Negative Selection Algorithm in Artificial Immune Systems
Negative Selection Algorithm in Artificial Immune Systems for Anomaly Detection