Abstract
It is necessary to enhance the performance of interactive genetic algorithms in order to apply it to complicated optimization problems successfully. An adaptive interactive genetic algorithm with grey for fitness of evolutionary individuals was proposed. The fitness uncertainty of evolutionary individuals was measured and expressed by grey. Through analyzing these fitness intervals, information reflecting the distribution of an evolutionary population was abstracted. Based on these, the probabilities of crossover and mutation operation of an evolutionary individual were presented. The proposed algorithm was applied to a fashion evolutionary design system. The results show that it can find many satisfactory solutions per generation.
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