Immune Genetic Algorithm (IGA) MATLAB Implementation

This document presents a comprehensive discussion of the six modular components in our MATLAB implementation of the Immune Genetic Algorithm. These modules systematically implement antigen recognition, initial antibody generation, fitness calculation, memory cell differentiation, antibody promotion/suppression mechanisms, and antibody reproduction through genetic operators (crossover and mutation). The Immune Genetic Algorithm is a biologically-inspired heuristic optimization method that mimics immune system mechanisms, demonstrating significant potential for solving complex optimization problems. In MATLAB implementation, the antigen recognition module typically initializes problem parameters and defines objective functions, while antibody generation creates initial population vectors using random initialization functions like rand() or randn(). Fitness evaluation employs mathematical formulations to assess solution quality, often implemented through vectorized operations for computational efficiency. Memory cell differentiation incorporates elite preservation strategies where high-fitness solutions are stored using array manipulation techniques. The antibody promotion/suppression module maintains population diversity through niching techniques and similarity thresholds, implemented using distance calculation functions like pdist(). The reproduction module applies genetic operators where crossover operations (e.g., single-point or uniform crossover) and mutation operations (using probability-based bit flipping) generate new candidate solutions. This algorithm's key advantage lies in its adaptive capabilities, dynamically adjusting search parameters through antibody concentration mechanisms. The MATLAB implementation allows customization of immune parameters (mutation rates, population size) and supports various problem domains including optimization challenges, data mining applications, and image processing tasks through appropriate fitness function design and constraint handling.