High Usability of Immune Genetic Algorithm Implementation

In my self-developed immune genetic algorithm implementation, I have observed its exceptionally strong usability. The algorithm's flexibility allows for easy adaptation to various scenarios by simply adjusting parameter module names in the code structure. This approach leverages key immunological principles such as antibody diversity and immune memory, combined with genetic algorithm operations including selection, crossover, and mutation. The main algorithm flow typically involves: 1) Initializing antibody population with random solutions, 2) Calculating affinity values using fitness functions, 3) Performing immune operations (clonal selection and suppression), 4) Applying genetic operators for evolution. The algorithm's strength lies in its dual optimization mechanism that maintains solution diversity while converging to optimal solutions, making it effective for solving complex optimization problems across different domains. Thus, it represents a valuable computational tool with wide applicability in engineering optimization, pattern recognition, and machine learning applications.