genetic algorithm
A search that evolves a whole population of candidates through fitness-biased selection, crossover, and mutation.
A genetic algorithm evolves a population of candidate points across generations: score every individual's fitness, select parents biased toward higher fitness (e.g. roulette-wheel selection), combine parents into children (crossover), randomly perturb some children (mutation), and form the next generation from the results, commonly keeping the current best individual unchanged (elitism).
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