Genetic Algorithms are evolutionary optimization methods inspired by natural selection and genetics. The algorithm maintains a population of candidate solutions (chromosomes) and evolves them through: (1) Selection - choosing fitter individuals based on fitness function; (2) Crossover - combining genetic information from parents to create offspring; (3) Mutation - introducing random changes to maintain diversity; (4) Replacement - forming new generation. GAs are particularly effective for: combinatorial optimization, nonlinear problems with multiple local optima, problems with discrete and mixed variables, and when gradients are unavailable. Common variants include: steady-state GA, elitist GA, and multi-objective GA (NSGA-II).