Genetic algorithms operate through a cycle of selection, crossover, and mutation. Initially, a population of possible solutions is generated. This population evolves over generations, with individuals being selected based on their "fitness"—a measure of how well they solve the problem. High-fitness individuals are then recombined (crossover) and altered (mutation) to produce a new generation of solutions.
