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A genetic algorithm optimization begins by generating a population of randomly
computed individuals within the constraints imposed on the model parameters.
Three basic operators: reproduction (selection), crossover and mutation are used
to ``evolve'' the solution from one generation (iteration) to the next.
Stanford Exploration Project
11/11/2002