MATLAB Implementation of Immune Genetic Algorithm for Optimization Problems

This guide provides detailed instructions on implementing immune genetic algorithms for optimization using MATLAB programming. The article explores fundamental principles and practical applications of immune genetic algorithms, with specific focus on MATLAB code development and execution workflows. Key implementation aspects covered include: designing appropriate fitness functions to evaluate solution quality, configuring critical algorithm parameters such as population size, mutation rates, and cloning factors, and establishing iterative optimization procedures. The implementation typically involves creating antibody population initialization functions, affinity calculation modules, and genetic operation handlers (crossover/mutation). Sample MATLAB code demonstrates antibody encoding/decoding methods, memory cell update mechanisms, and convergence monitoring techniques. Practical exercises are provided to help readers understand population diversity maintenance strategies and antibody suppression operations. Special attention is given to balancing global exploration and local exploitation through vaccination operations and immune selection processes. This resource assists researchers and engineers in developing efficient optimization solutions using immune-inspired genetic algorithms in MATLAB environments.