Genetic Algorithms (GAs) are a class of optimization and search techniques inspired by the process of natural selection. They simulate the process of evolution by maintaining a population of candidate solutions and applying genetic operators like selection, crossover, and mutation to evolve solutions over generations. In GAs, solutions are often represented as chromosomes (strings of values), and fitness functions are used to evaluate their performance. The best solutions are selected for reproduction, with crossover creating new offspring and mutation introducing random changes. GAs are used in various fields, such as optimization, machine learning, and artificial intelligence, for solving complex problems.
