A Genetic Algorithm (GA) is a search and optimization technique inspired by the principles of natural selection and evolution. It is widely used in machine learning for solving optimization problems, especially when traditional methods struggle with complex landscapes. GAs work by creating a population of potential solutions, encoded as chromosomes. Through iterative processes like selection, crossover (recombination), and mutation, the algorithm evolves this population toward better solutions.
Each generation is evaluated using a fitness function that measures how well the solutions perform. GAs are highly versatile and applied in areas like feature selection, hyperparameter tuning, and neural network training.
