Application of interactive genetic algorithm to fashion design

Abstract

In general, computer-aided design support systems have got an approach of traditional 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 just depends on large amount of collected data. To overcome this sort of problem interactive genetic algorithm (IGA) has been recently proposed, as a new trend of evolutionary computation. IGA uses human's response as fitness value when the fitness function cannot be explicitly defined. This enables IGA to be applied to artistic domains, and we propose a fashion design aid system using it. Unlike the previous works that attempt to model the dress design by several spline curves, the proposed system is based on 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 VRML to enhance the system interface. The experiments with several human subjects show that the IGA approach to dress design aid system is promising. #

Fashion sketch design by interactive genetic algorithms

“…For the sake of simplicity, skirts (Independent clothes covering the lower half of one's body, or a part of clothes under waistline) [13] are used as examples to illustrate the proposed sketch design system. Needless to say, the proposed sketch design system can deal with more complicated fashion products such as jackets and suits.…”

“…This IGA is different from previous research in four aspects: first, in order to ensure a good compromise between the computational time and the quality of solution, an appropriate population of 9 individuals is chosen to ameliorate the shortcomings of a large population size and a small population size. Second, unlike other studies [13][14][15], in our method, the value encoding approach is developed to diminish the size of the search space. Third, users are allowed to select one to three individuals per generation so as to achieve a good balance between design variability and rapid convergence.…”

“…Y represents the vector Y=(y 1 , y 2 , y 3 , y 4 , y 5 , y 6 , y 7 ). The vector of A=(a 0 , a 1 , a 2 , a 3 , a 4 , a 5 , a 6 , a 7 , a 8 , a 9 , a 10 , a 11 , a 12 , a 13 13 ) are the low and upper bound of X. The first parameter (x 0 ) is used to describe the skirt silhouette type.…”

“…However, most of its productions were impractical designs because they did not consider domain-specific knowledge. To solve this problem, Kim and Cho [13] proposed an effective human-oriented evolutionary system based on the knowledge of fashion design to encode genotype with OpenGL design models in order to produce more realistic and reasonable design. Unlike traditional approaches that attempt to model the dress design by several spline curves, Kim and Cho [13] provided a new encoding scheme to describe a dress with three parts: neck and body, sleeve, and skirt.…”

Semi-automatic Search-Based Test Generation

“…In Interactive GAs (e.g., [5]) human agents are used to evaluate the fitness of individuals where an automatic calculation is difficult, such as for example in fashion design. In Human GAs [6] all primary genetic operators are delegated to external human agents.…”