MATLAB Implementation of Adaptive Genetic Algorithm

In this article, we discuss the characteristics of adaptive genetic algorithms and the challenges they face when solving complex problems. While adaptive genetic algorithms demonstrate rapid computational performance, they frequently encounter premature convergence when handling intricate optimization tasks - a primary issue we address. To resolve this problem, we have developed a novel program that effectively mitigates premature convergence through dynamic adjustment of crossover and mutation probabilities based on population fitness diversity. The implementation features fitness-scaling mechanisms and adaptive operator selection that maintain population diversity while accelerating convergence. Key MATLAB functions include adaptive crossover rate calculation using population statistics and mutation probability optimization through fitness variance analysis. We will detail the program's implementation methodology in subsequent sections, including chromosome encoding techniques, elite preservation strategies, and termination condition optimization, along with its successful applications in real-world optimization scenarios.