Creative Design Using Collaborative Interactive Genetic Algorithms

Creative Design Using Collaborative Interactive Genetic Algorithms

Juan C. QuirozPhD Dissertation DefenseThursday April 29, 2010

Department of Computer Science & EngineeringUniversity of Nevada, Reno

Outline

1. Creativity in Design

2. Collaborative Interactive Genetic Algorithms

3. Reducing User Fatigue in Interactive Genetic Algorithms

4. Testing Our Computational Model of Creative Design

5. Contributions

Design

• Conceptual design• Detailed design• Evaluation • Iterative redesign

Conceptual Design

• Initially conceiving and elaborating solutions that meet a set of requirements

• Change in requirements• Subjective evaluation of alternative design

concepts– Aesthetics and other subjective criteria

• Collaboration

Creativity

• Novel and useful• Role of collaboration

Individual Work

Individual

PeersWork

Computational Model of Creative Design

• Allows for subjective exploration of solutions• Supports collaboration• Has the potential to generate creative

solutions

Main Claim

Collaborative interactive genetic algorithms are a viable computational

model of creative design

Outline





5. Contributions

Collaborative Interactive Genetic Algorithm

• Population based search technique– Natural selection– Survival of the fittest

CollaborativeInteractive Genetic Algorithms (IGAs)

• Fashion design (Kim 2000)• Micromachine design (Kamalian 2005)• Music, editorial design (Takagi 2001)• Traveling salesman problem (Louis 1999)

Collaborative Interactive Genetic Algorithm

Creative Design

• Floorplans with rectangular rooms• Purposely shifting the focus of

the search space– Circular rooms– Ellipsoid rooms– Star-shaped rooms

Creative Design

Sharing Solutions

IGAP: Interactive Genetic Algorithm Peer to Peer

Outline





5. Contributions

User Fatigue in Interactive Genetic Algorithms

• Genetic Algorithms tend to rely on– Large populations– Many generations

• Suboptimal solutions• Noisy fitness

Fitness Interpolation

• Pick the best solution every nth generation

Experimental Setup

• Test on the onemax problem• Subset methods– Best n, best n/2 and worst n/2, random n, PCA n

• Subset size• Gaussian noise• Collaboration

Experimental Setup

• Simulated user input– 20 user evaluations– Greedy user always picks the solution with most

ones• 30 independent runs• Step sizes of 1, 2, 5• Subset size 9

Boxplots

Maximum

Minimum

Median

Upper quartile

Lower quartile

Outlier

Outlier

Subset Methods

Subset Size

Step size 1Step size 2

Step size 5

Subset Size

No Noise vs Gaussian Noise with Sigma=1

Step size 1Step size 2

Step size 5

Noise

Number of Peers

Summary

• Users can effectively bias evolution towards high fitness solutions– Subset size– Noise– Collaboration

Outline





5. Contributions

Goals

• User studies– Solutions created individually– Solutions created collaboratively

• Show that solutions created collaboratively are more creative

First User Study: Floorplanning

7 8 9 10 11 12

Bedroom

Living RoomEating area

Bathroom

Collaborative Floorplanning

User’s Individuals Peers’ Individuals

Pilot: Experimental Setup

• Requirements– Design a floorplan for a 2 bedroom, 1 bathroom

apartment– Living room should face north-west– The two bedrooms should not have a common

wall– At least one of the bedrooms should have direct

access to the bathroom

1 2 3 4 5 6

Pilot: Experimental Setup

• Four colleagues and I evolved floorplans– Individually– Collaboratively

• Ten computer science graduate students evaluated the designs by taking a survey

• The plans were evaluated for creative content based on practicality and originality

1 2 3 4 5 6

Floorplan Results

1 2 3 4 5 6

7 8 9 10 11 12

ResultsDESIGN # PRACTICALITY ORIGINALITY RANK

1 2.7 3.1 6 2 2.9 2.7 8 3 2.4 3.3 7 4 4.4 3.3 2 6 4.0 3.2 3 7 2.2 3.4 8 8 2.8 3.4 5 10 3.2 3.5 4 11 4.2 3.6 1 12 1.6 3.8 10

7 8 9 10 11 12

1 2 3 4 5 6

Floorplanning User Study: Experimental Setup

• Requirements– Create a floorplan for a 2 bedroom, 1 bathroom

apartment– Bathrooms close to the bedrooms– Bathrooms far from kitchen and dining areas

Floorplanning User Study: Experimental Setup

• Participants:– 8 women, 12 men

• Five groups of size four• Agenda

1. Tutorial2. Create individual floorplan3. Create collaborative floorplan4. Evaluation of floorplans

Evaluation Criteria1. Appealing – unappealing2. Average – revolutionary3. Commonplace – original4. Conventional –

unconventional5. Dull – exciting6. Fresh - routine7. Novel – predictable8. Unique – ordinary9. Usual - unusual10. Meets all requirements -

does not meet requirements

• Creative Product Semantic Scale

• Seven point Likert scale

Hypothesis

• Is collaboration amongst peers sufficient to allow for the potential to produce creative solutions?

• Designs evolved collaboratively will consistently rank higher in the evaluation criteria.

ResultsEvaluation Criterion Desired Ind. Avg. Coll. Avg. P-value

Appealing - Unappealing Low 4.08 4.39 0.439

Average - Revolutionary High 3.76 4.34 0.047Commonplace - Original High 3.97 4.68 0.021

Conventional - Unconventional

High 4.03 4.41 0.355

Dull – Exciting High 3.65 3.93 0.326

Fresh – Routine Low 3.82 3.68 0.810

Novel - Predictable Low 3.55 3.40 0.697Unique - Ordinary Low 3.49 3.11 0.251Usual - Unusual High 4.21 4.51 0.395

Meets All Req. -Does Not Meet Req.

Low 2.63 2.83 0.779

Discussion

• Ambiguity in evaluation criteria– Appealing – unappealing– Positive – Negative (?)– Negative – Positive (?)

• Applicability of evaluation criteria– “Exciting”– Domain expert vs. student

• Participants created only 1 collaborative floorplan and 1 individual floorplan

• Simple graphic representation

Second User Study: 3D Modeling

• Vertex programs– p.x += 20– p.x += sin(time)

Sample Ninja Transformations

Collaborative Setup

• User 1– Equations that modify the

x and z coordinates

• User 2– Equations that modify the

y and z coordinates

• After collaboration– Equations that modify the

x, y, and z coordinates

Experimental Setup

• Design Phase– Two groups of 10 participants

• Evaluation Phase– On-site evaluation• 20 participants

– Online evaluation• 16 participants

Experimental Setup

• Groups of 2• Agenda

1. Tutorial2. Creating 3D models3. Picking solutions for

the evaluation phase

Individually

Collaboratively

Individually

Collaboratively

Individually

Collaboratively

Design Phase

Female

Ninja

Robot

Robot

Ninja

Female

Individually

Collaboratively

Individually

Collaboratively

Individually

Collaboratively

Female

Ninja

Robot

Female

Ninja

Robot

Evaluation Phase

• 7 point Likert scale• Creative Product Semantic Scale• The transformation is:

– Extremely creative – Not Creative At All

• The transformation can be used in a video game.

• The transformation with minor tweaks can be used in a video game.

• The transformation is novel.• The transformation is surprising.

Individually

Collaboratively

Individually

Collaboratively

Individually

Collaboratively

Results

Individual Collaborative

The transformation is creative.

The transformation can be used in a video game.

The transformation with minor tweaks can be used in a video game.

The transformation is novel.

The transformation is surprising.

Online Evaluation

• Best individually created models• Best collaboratively created models• Evaluation Criteria– The transformation is creative.– The transformation can be used in a video game.– The transformation is novel.– The transformation is surprising.– Which of the two rows did you like the most?– Which of the two rows is the most creative?

Results

• Individually created models vs collaboratively created models– No statistically significant results

Results

• Which of the two rows did you like the most?– 8 participants picked the

individual row– 7 participants picked the

collaborative row– 1 participant did not answer

• Which of the two rows is the most creative?– 3 participants picked the

individual row– 13 participants picked the

collaborative row

IndividualCollaborativeNA

IndividualCollaborative

Discussion

• Different 3D models• Lack of context• Online Evaluation Nuances– Switching windows– 15 second average– Rewinding– Scoring the row of individually created models first– Indecisive participants and median scores

Outline





5. Contributions

Contributions

• A new computational model of creative design– Subjective exploration of solutions– Integrates collaboration

• Implementation of IGAP framework: Interactive Genetic Algorithm Peer to Peer

• Analysis of our fitness interpolation technique in the onemax problem

Contributions

• Floorplanning pilot– Collaborative solutions were considered more

original• Floorplanning user study– Collaborative solutions were considered more

original and revolutionary• 3D Modeling user study– 13 out of 16 participants picked row of

collaborative of solutions as the most creative

Future Work

• Conduct additional user studies– Long term user studies with design teams

• Refine and test IGAP framework• Machine learning

Acknowledgments

• Dr. Sushil Louis• Dr. Bobby Bryant• Dr. Swatee Naik• Dr. Sergiu Dascalu• Dr. Amit Banerjee• Dr. Darren Platt• Study participants

– Students, adult volunteers, and faculty• This work was supported in part by contract number N00014-

05-1-0709 from the Office of Naval Research and the National Science Foundation under Grant no. 0447416.

Questions?

Thank you!

www.cse.unr.edu/~quiroz