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Scaling factor: The scaling factor, (0, ), controls the ampli cation of the di erential variations, (xi2 xi3 ) The smaller the value of , the smaller the mutation step sizes, and the longer it will be for the algorithm to converge Larger values for facilitate exploration, but may cause the algorithm to overshoot good optima The value of should be small enough to allow di erentials to explore tight valleys, and large enough to maintain diversity As the population size increases, the scaling factor should decrease As explained in Section 1311, the more individuals in the population, the smaller the magnitude of the difference vectors, and the closer individuals will be to one another Therefore, smaller step sizes can be used to explore local areas More individuals reduce the need for large mutation step sizes Empirical results suggest that large values for both ns and often result in premature convergence [429, 124], and that = 05 generally provides good performance [813, 164, 19] Recombination probability: The probability of recombination, pr , has a direct in uence on the diversity of DE This parameter controls the number of elements of the parent, xi (t), that will change The higher the probability of recombination, the more variation is introduced in the new population, thereby increasing diversity and increasing exploration Increasing pr often results in faster convergence, while decreasing pr increases search robustness [429, 164] Most implementations of DE strategies keep the control parameters constant Although empirical results have shown that DE convergence is relatively insensitive to di erent values of these parameters, performance (in terms of accuracy, robustnes, and speed) can be improved by nding the best values for control parameters for each new problem Finding optimal parameter values can e a time consuming exercise, and for this reason, self-adaptive DE strategies have been developed These methods are discussed in Section 1333.

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