Evolutionary Algorithms

Evolutionary algorithms are a class of optimization methods that simulate natural selection and genetic mechanisms, aiming to iteratively improve the quality of candidate solutions to achieve optimal or near-optimal solutions. Their primary goal is to solve complex optimization problems, especially in scenarios with large search spaces, non-linearity, or multiple local optima. Evolutionary algorithms perform efficient exploration in multi-solution spaces through operations such as selection, crossover, and mutation, demonstrating strong robustness and adaptability. These algorithms are widely applied in fields such as engineering design, machine learning, and combinatorial optimization, effectively addressing problems that traditional optimization methods struggle to handle.