Genetic Algorithm with Knowledge-based Encoding for Interactive Fashion Design Hee-Su Kim and Sung-Bae Cho Computer Science Department, Yonsei University Shinchon-dong, Sudaemoon-ku, Seoul , Korea [madoka, PRICAI-2000

2 Agenda Motivation Backgrounds System development Knowledge-based encoding Experimental results Conclusion and future works

3 Before the Industrial Revolution : Customers have few choices on buying their clothes After the Industrial Revolution : Customers can make their choices with very large variety Near Future : Customers can order and get clothes of their favorite design Manufacturer Oriented Consumer Oriented Changes on Consumer Economy Motivation

4 Almost all consumers are non-professional at design To make designers contact all consumers is not effective Need for the design system that can be used by non-professionals Need for Interaction-based System Motivation

5 Fashion design –To make a choice within various styles that clothes can take Three shape part of fashion design –Silhouette –Detail –Trimming Backgrounds Fashion Design

6 t=0; Initialize Population Evaluate P(t); while not done do t=t+1; P=Select Parents P(t) Recombine P(t); Mutate P(t); Evaluate P(t); P=Survive P, P(t); end while CrossoverMutation Genetic Algorithm Backgrounds

7 Interactive Genetic Algorithm Backgrounds

8 Related Works Virtuosi System (Nottingham Trent University, 1998) AutoCAD with ApparelCAD plug-in (Autodesk co.) –Fashion design aid system for professionals only Manual Evolutionary Design Aid System (Nakanishi, 1996) –Often produces impractical designs Backgrounds Interactive GA KB Encoding Apply evolutionary Computation using domain specific knowledge

9 Overview System Development

10 VRML : Simply get 3D but too slow OpenGL : Faster but not easy to implement Use GLUT library with OpenGL –Reduce the burden of programming OpenGL 3D Modeling Method System Development

11 Modeling by 3D Studio MAX System Development

12 IGA Fashion Design Aid System System Development

13 Knowledge-based Encoding Gene Encoding Search space size =34*8*11*8*9*8 =1,880,064 A : Neck and body style(34) E : Skirt and waistline style(9) C : Arm and sleeve style(11) B : Color(8) D : Color(8) … … … A BCDEF Total 23 bits F : Color(8)

14 Example Design from a Genotype Knowledge-based Encoding

15 Schema Theorem The instances of schema H in particular generation t+1, m(H, t+1), can be expressed in terms of m(H, t) Schemata with short defining length, low order, above-average fitness receive exponentially increasing trials in subsequent generations Knowledge-based Encoding

16 Experimental Environment Subjects –10 male and female student, no background on fashion design Crossover rate : 0.5 (1-point crossover) Mutation rate : 0.05 (Binary mutation) 10 generations with elitist preserving Request for each subjects –Find out most cool-looking design with given system Experimental Results

17 Convergence Test for Cool-Looking Design Experimental Results

18 Subjective Test Experimental Results Examples of searched design which gives cool feeling

19 Fitness Changes for each Encoding Method Experimental Results

20 Relative Satisfaction for each Encoding Method Experimental Results

21 Example Solution Design and Frequency of Each Solution Schema Experimental Results

22 Conclusion and Future Works Knowledge-based Encoding in Interactive Genetic Algorithm for a Fashion Design Aid System –Based on Knowledge of fashion design –Compared with sequential encoding by several experiments Future Works –Adding up extra design elements such as textile : To enlarge the search space –Clustering : To avoid Genetic drift caused by small population size –Direct Manipulation : To accelerate convergence with relatively short generation