Development of an IGA-based Fashion Design Aid System with Domain Specific Knowledge

In general, computer aided design support systems have got an approach of artificial intelligence, which statistically analyzes data such as the behavior of designer, to extract formal design behavior. This approach, however, can neither deal with continuous change of fashion nor reflect personal taste well, as it depends on large amount of collected data. To overcome this problem, this paper applies an interactive genetic algorithm (IGA) to the problem of fashion design. IGA is a sort of genetic algorithm that uses human’s response as fitness value when the fitness function cannot be defined explicitly. Unlike the previous works that attempt to model the dress design by several spline curves, we propose a new encoding scheme that practically describes a dress with three parts: body and neck, sleeve, and skirt. By incorporating the domain specific knowledge into the genotype, we could develop a more realistic design aid system for women’s dress. We have implemented the system with OpenGL and VRh4L to enhance the system interface. The experiments with several human subjects show that the IGA approach to dress design aid system is promising.