Standard Genetic Algorithm and Immune Algorithm Integration

This MATLAB implementation provides both a standard genetic algorithm and a hybrid immune-genetic algorithm, utilizing a main function to coordinate various genetic operators for evolutionary optimization. The genetic algorithm is an evolution-based optimization technique that mimics biological evolutionary processes to search for optimal solutions. Through iterative operations including selection, crossover, and mutation applied to population individuals, the algorithm progressively converges toward optimal solutions. The immune algorithm simulates immune system mechanisms, employing processes like antibody generation, selection, and cloning to explore optimal solutions. By integrating genetic and immune algorithms, this implementation leverages the strengths of both approaches to enhance optimization performance. The code structure employs modular design where the main function (main_ga.m) calls specialized operator functions: selection operators implement fitness-proportionate or tournament selection methods, crossover operators handle single-point or uniform crossover operations, and mutation operators introduce random perturbations using Gaussian or bit-flip mutations. For the immune algorithm component, the implementation includes affinity calculation functions and cloning mechanisms with hypermutation. Key MATLAB functions like rand, sort, and logical indexing are extensively used for population management and operator implementations. This architecture enables flexible parameter tuning and operator customization, allowing researchers to adapt the algorithm for specific optimization problems while maintaining code readability and performance.