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Genetic algorithms (GA's) have some control pa rameters such as the probability of bit mutation or the probability of crossover. These are nornially given a priori by the user (programmer) of the algorithm. There exists a wide variety of values for control parameters and it is difficult to find the best choice of these values in order to optimize the be haviour of a particular GA. We introduce a self...