Immune Genetic Algorithm for Global Optimal Solution Search

In this text, we introduce the application of the Immune Genetic Algorithm. The Immune Genetic Algorithm is an optimization algorithm designed for global optimal solution search, typically implemented through operations like antibody population initialization, fitness evaluation, and immune operator execution. Verified implementations demonstrate that it not only achieves high computational efficiency but also exhibits rapid convergence characteristics. By simulating evolutionary processes of biological immune systems, it combines genetic operations (crossover, mutation) with immunological principles (antibody diversity, memory cells) to solve complex optimization problems. Through the incorporation of immune mechanisms, the algorithm maintains population diversity within the search space and adaptively adjusts search strategies using affinity calculation functions. Consequently, the Immune Genetic Algorithm finds extensive applications across various domains including engineering optimization, data mining, and machine learning, where its antigen recognition simulation and antibody cloning operations prove particularly effective. In summary, the Immune Genetic Algorithm represents a highly efficient optimization approach with superior convergence performance, often implemented through fitness-proportional selection and dynamic mutation rate adjustments in practical coding scenarios.