Genetic Algorithm

Genetic algorithms (GAs) are optimization techniques inspired by natural selection, aiming to find optimal or near-optimal solutions for complex problems by iteratively improving a population of candidate solutions. Current research emphasizes enhancing GA efficiency and applicability, focusing on novel selection strategies (e.g., upper bound-based selection), hybrid approaches integrating GAs with deep kernel learning or neural networks, and addressing challenges in high-dimensional spaces and specific problem domains (e.g., the traveling salesman problem, feature selection). These advancements are impacting diverse fields, including drug discovery, wildfire management, and resource allocation, by providing robust and flexible optimization tools for computationally challenging problems.