Interactive genetic algorithms with grey level of individuals fitness

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 level is proposed in this paper in which the uncertainty of evolutionary individuals is measured by grey level. Through analyzing these fitness intervals, information reflecting the distribution of an evolutionary population is abstracted. Based on these, the probabilities of crossover and mutation operation of evolutionary individuals are presented. The algorithm proposed in this paper is applied to a fashion evolutionary design system, and the results show that it can find many satisfactory solutions per generation. The achievement of the paper offers a new approach to enhance the performance of interactive genetic algorithms.