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In a genetic algorithm, a population of candidate solutions (called individuals, creatures or phenotypes) to an optimization problem is evolved toward better solutions.

Each candidate solution has a set of properties (its chromosomes or genotype) which can be mutated and altered.

The evolution usually starts from a population of randomly generated individuals. In each generation, the fitness of every individual in the population is evaluated; the fitness is usually the value of the objective function in the optimization problem being solved.

The more fit individuals are stochastically selected from the current population, and each individual's genome is modified (recombined and possibly randomly mutated) to form a new generation.

The new generation of candidate solutions is then used in the next iteration of the algorithm.

Commonly, the algorithm terminates when either a maximum number of generations has been produced, or a satisfactory fitness level has been reached for the population.

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