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Genetic Algorithm with Knowledge-based Encoding for Interactive Fashion Design
Hee-Su Kim and Sung-Bae Cho
Computer Science Department, Yonsei UniversityShinchon-dong, Sudaemoon-ku, Seoul 120-749, Korea
[madoka, sbcho]@candy.yonsei.ac.kr
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
ConsumerOriented
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
Crossover Mutation
Genetic Algorithm
Backgrounds
7
Crossover / Mutation Fitness Evaluation
Reproduction
Initial Population
Population
User Selection
GA IGA
Fitness Function
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
Decode
User Fitness
Combine
Display
Interactive Genetic Algorithm
OpenGL Program
GA operation Reproduce
Models ofeach part
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 B C D E F
Total 23 bits
F : Color(8)
14
Example Design from a Genotype
Knowledge-based Encoding
001010 101 0101 011111 0111
High Green WhiteTrumpetPurpleMelon
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
mc
Homc
pHol
Hp
f
HftHmtHm
pl
Hp
f
HftHmtHm
)(1
)(1
)(),()1,(
11
)(1
)(),()1,( )(
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
0
10
20
30
40
50
60
70
80
1 2 3 4 5 6 7 8 9 10
Generation
Fitn
ess
Valu
e
Average Fitness Value Best Fitness Value
18
Subjective Test
Experimental Results
Examples of searched design which gives cool feeling
19
Fitness Changes for each Encoding Method
Experimental Results
0
10
20
30
40
50
60
70
1 2 3 4 5 6 7 8 9 10
Generation
Ave
rage
Fitn
ess
Knowledge- based Encoding
Sequential Encoding
20
Relative Satisfaction for each Encoding Method
Experimental Results
21
Example Solution Design and Frequency of Each Solution Schema
- 10123456789
10
0 1 2 3 4 5 6 7 8 9 10
Generations
Freq
uenc
y
010100***************** (Slit body design)******011************** (White body color)*********1011********** (Sleeveless arm design)****************0100*** (Scooter skirt design)********************100 (Blue skirt color)
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 se
arch space– Clustering : To avoid Genetic drift caused by small population size– Direct Manipulation : To accelerate convergence with relatively short
generation