Hybrid Genetic

Hybrid genetic algorithms combine the global search capabilities of genetic algorithms with the local optimization power of other methods, such as simulated annealing, hill climbing, or even neural networks, to solve complex optimization problems. Current research focuses on applying these hybrid approaches to diverse fields, including feature selection, logistics (e.g., vehicle routing, container loading), and resource allocation in healthcare and agriculture, often resulting in significant performance improvements over traditional methods. The resulting enhanced efficiency and solution quality have broad implications across various scientific disciplines and practical applications, offering optimized solutions for computationally challenging problems.