Genetic Algorithm Enhanced with Immune Mechanism

In this article, we explore an enhanced genetic algorithm that integrates immune mechanisms. The improved genetic algorithm is an optimization technique that simulates natural evolutionary processes. Genetic algorithms represent biologically inspired methods that mimic natural selection and evolutionary mechanisms to solve optimization challenges. The immune mechanism is a computational approach modeled after biological immune systems, which helps maintain population diversity during search processes to prevent premature convergence to local optima. By incorporating immune mechanisms into genetic algorithms, we can enhance performance and robustness, making the algorithm more effective for practical problem-solving. From an implementation perspective, key enhancements include: - Antibody diversity maintenance through similarity threshold calculations - Memory cell preservation mechanisms for retaining high-quality solutions - Dynamic vaccination operations that introduce beneficial genes into new generations - Affinity evaluation functions balancing objective optimization and population diversity The algorithm typically involves these core components: 1. Initialization with immune-inspired population sampling 2. Fitness evaluation incorporating both objective function and diversity metrics 3. Selection operations with immune-inspired elite retention 4. Crossover and mutation with antibody similarity constraints 5. Vaccination procedures introducing prior knowledge into solutions 6. Population updating with memory cell management This integration demonstrates superior performance in complex optimization landscapes by maintaining solution diversity while accelerating convergence to global optima.