MATLAB Implementation of Immune Genetic Algorithm

The Immune Genetic Algorithm (IGA) is a powerful population-based optimization technique capable of solving various complex problems. This algorithm is inspired by biological immune system principles, simulating immune system evolution and selection processes to search for optimal solutions. The MATLAB implementation provides a practical framework for executing IGA, featuring key components such as antibody initialization, affinity calculation, immune selection, cloning operation, and mutation procedures. The code typically includes functions for population generation, fitness evaluation, crossover operations, and elite preservation mechanisms. For enhanced performance, the implementation may incorporate memory cells for retaining optimal solutions and vaccination operations for directing the search process. When working with this algorithm, proper parameter tuning of population size, mutation rate, and selection pressure is crucial for optimal performance. For additional references or assistance, consulting relevant literature and resources is recommended to deepen understanding and improve application of the immune genetic algorithm in practical scenarios.