Genetic Algorithms are optimization techniques inspired by the process of natural selection. They work by creating a population of potential solutions to a problem and then iteratively improving them. Each solution is represented as a string of data, often resembling a chromosome, and is evaluated based on a fitness function that measures how well it solves the problem. During each iteration, the best solutions are selected to reproduce, combining their features to create new solutions. This process includes mutation, where random changes are introduced, helping to explore a wider range of possibilities. Over time, the population evolves, ideally leading to better solutions.