Abstract

Traditional genetic algorithms (GAs) are highly likely to get stuck at a local optimum, and often have slow convergent speed. A novel adaptive genetic algorithm (AGA) called cloud-model-based AGA (CAGA) is introduced. Unlike conventional genetic algorithms, CAGA presents the use of cloud model to adaptively tune the probabilities of crossover pc and mutation pm depending on the fitness values of the solutions. Since the normal cloud model has the properties of randomness and stable tendency, CAGA is expected to realize the twin goals of maintaining diversity in the population and sustaining the convergence capacity of the GA. The performance of the CAGA is compared with the standard GA (SGA) and AGA in optimizing several typical functions with varying degrees of complexity and design of IIR digital filter. In all cases, the CAGA converges to the global optimum in far fewer generations than the SGA and AGA, and it gets stuck at a local optimum fewer times.

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