A genetic algorithm is an evolutionary optimization method inspired by natural selection. It evolves a population of candidate solutions through selection, crossover, mutation, and fitness evaluation to search for strong solutions to a problem.
A metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on bio-inspired operators such as mutation, crossover and selection.