Interactive Genetic Algorithms with Grey Level of Discrete Fitness

AbstractFor the problem that interactive genetic algorithms lack a way of measuring the uncertainty of evaluation, a method with grey level for discrete fitness is proposed to deal with this problem. Through analyzing the grey level of discrete fitness, information reflecting the distribution of an evolutionary population is abstracted. Based on these, the adaptive probabilities of crossover and mutation operation of an evolutionary individual are proposed. The algorithm is applied to a fashion evolutionary design system, the simulation results indicate that the algorithm can effectively resolve human fatigue and improve the performance of optimization