Immune Genetic Algorithm Offers Strong Practical Applicability

The Immune Genetic Algorithm (IGA) is an optimization methodology that integrates principles from biological immune systems with genetic algorithms, exhibiting strong adaptability and robustness. Its core mechanism simulates antibody selection and mutation processes from biological immunity to refine problem solutions through iterative population evolution. The algorithm's flexibility is evident in its modular architecture, where simple parameter adjustments or component substitutions enable adaptation to diverse scenarios. For instance, modifying the fitness function calculation method (e.g., implementing weighted multi-objective evaluation), tuning mutation probabilities using adaptive strategies, or altering antibody selection mechanisms (such as roulette-wheel or tournament selection) can significantly impact algorithmic performance. This design approach enhances scalability and simplifies secondary development—key functions like `calculateFitness()`, `mutatePopulation()`, and `selectAntibodies()` can be independently optimized or replaced without restructuring the core framework. In practical applications, IGA effectively addresses complex problems including multi-objective optimization, combinatorial optimization (e.g., traveling salesman problem), and hyperparameter tuning in machine learning models. A well-implemented modular design allows seamless customization for specific requirements, such as incorporating memory cell mechanisms for faster convergence or designing affinity maturation workflows for precision optimization. This adaptability underscores the algorithm’s practicality for real-world engineering challenges.