Genetic Algorithm

A genetic algorithm is a method used to find good solutions to complex problems by mimicking natural evolution. It starts with a population of possible solutions, encoded as "chromosomes," and evaluates their effectiveness with a "fitness" score. Through processes similar to biological reproduction—selection, crossover (combining parts), and mutation (random changes)—the algorithm creates new generations of solutions. Over successive iterations, the population evolves toward better solutions. This approach helps efficiently explore large or complicated problem spaces to identify optimal or near-optimal answers, akin to how species adapt over time in nature.