Genetic Algorithms (GAs) are a subset of Evolutionary Algorithms (EAs) that mimic the natural process of evolution. GAs are used to find the optimal solution to a problem by generating a population of potential solutions and using genetic operators such as selection, crossover, and mutation to produce offspring solutions that inherit favorable traits from their parents. The offspring solutions are evaluated based on a fitness function, and the fittest solutions are selected to become parents for the next generation. This process continues until a satisfactory solution is found or a predetermined stopping criterion is met.
GAs are used to solve optimization problems where the search space is large and complex. They have been applied to a wide range of problems, including engineering design, scheduling, financial forecasting, and data mining. GAs can handle problems with multiple objectives, constraints, and non-linearity, making them a popular choice for optimization tasks in real-world applications.
