MATLAB Implementation of Artificial Immune Algorithm

This text introduces a MATLAB-based implementation of artificial immune algorithm, which serves as an excellent learning resource for beginners. The artificial immune algorithm is an optimization technique inspired by biological immune systems, applicable to solving various computational problems. The MATLAB implementation includes essential components such as population initialization using rand() function, fitness evaluation through custom objective functions, cloning operations with proportional selection, and hypermutation mechanisms controlled by mutation rates. Beginners can easily study the algorithm's core principles through well-commented code sections that demonstrate antigen-antibody interaction simulation, memory cell updates, and diversity maintenance strategies. By experimenting with parameter modifications in the configuration section (e.g., population size, mutation probability, cloning factor) and designing custom test functions, users can deepen their understanding of immune algorithm mechanics. This program provides practical hands-on experience with optimization algorithm implementation, making it a valuable educational tool for computational intelligence courses and self-study projects.