Genetic algorithms are optimization algorithms inspired by the process of natural selection and genetics. They are used to find solutions to optimization and search problems by mimicking the process of natural selection, where individuals with favorable traits are more likely to survive and reproduce. In genetic algorithms, a population of potential solutions is evolved over multiple generations by selecting the fittest individuals, which are then combined and mutated to create new solutions. This allows the algorithm to explore a wide range of possible solutions and converge towards an optimal or near-optimal solution. Genetic algorithms are commonly used in various fields such as engineering, computer science, and economics to solve complex optimization problems that may be difficult to solve using traditional methods. They are particularly well-suited for problems with a large search space and non-linear relationships between variables.