Interactive Genetic Algorithms with Individual Fitness Not Assigned by Human
Dunwei Gong (China University of Mining and Technology, P.R.China)
Xin Yao (University of Birmingham, United Kingdom)
Jie Yuan (China University of Mining and Technology, P.R.China)
Abstract: Interactive genetic algorithms (IGAs) are effective methods to solve optimization problems with implicit or fuzzy indices. But human fatigue problem, resulting from evaluation on individuals and assignment of their fitness, is very important and hard to solve in IGAs. Aiming at solving the above problem, an interactive genetic algorithm with an individual fitness not assigned by human is proposed in this paper. Instead of assigning an individual fitness directly, we record time to choose an individual from a population as a satisfactory or unsatisfactory one according to sensitiveness to it, and its fitness is automatically calculated by a transformation from time space to fitness space. Then subsequent genetic operation is performed based on this fitness, and offspring is generated. We apply this algorithm to fashion design, and the experimental results validate its efficiency.
Keywords: genetic algorithm, human fatigue, individual fitness, interactive genetic algorithm, optimization
Categories: G.1.6, H.1.2, I.2.8