Adaptive Genetic Algorithm

The Adaptive Genetic Algorithm is a fundamental optimization technique that allows autonomous modification of the objective function based on problem characteristics, leading to improved results. The algorithm's core strength lies in its adaptability, where mutation and crossover operations dynamically adjust to continuously refine solutions and converge toward optimal outcomes. When implementing this algorithm, developers typically need to customize the objective function through code modifications to better align with specific problem requirements. Key implementation aspects include defining fitness evaluation functions, adjusting crossover and mutation rates based on population diversity, and implementing termination conditions based on convergence metrics. The algorithm framework often involves population initialization, fitness calculation, selection operations using techniques like roulette wheel or tournament selection, and adaptive genetic operators that modify their parameters according to solution quality and diversity metrics.