cec 2009: towards creative design using collaborative interactive genetic algorithms

Towards Creative Design Using Collaborative Interactive Genetic

Algorithms

Juan C. Quiroz, Sushil J. Louis, Amit Banerjee, and Sergiu Dascalu

Evolutionary Computing Systems LabDepartment of Computer Science & Engineering

University of Nevada, Reno

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Outline

• Motivation• Computational model of creativity• Design space exploration• IGAP• Experimental Setup• Discussion and Results

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Creativity

• John Gero’s definition of creative designing:– The addition of variables during the design

process has the potential, but does not guarantee, to generate creative content.

• Is collaboration amongst peers sufficient to allow for the potential to produce creative content in designing?

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Motivation

• Design process1. Conceptual design2. Detailed design3. Evaluation4. Iterative redesign

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Conceptual Design

• Subjective evaluation of alternative design concepts– Aesthetics and other subjective criteria

• What is the formula for how designers evaluate subjective criteria?

• Collaborative design

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Collaborative Interactive

Genetic Algorithms

Case study: Floorplans

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Computational Model of Creative Design:Collaborative Interactive Genetic Algorithms

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Design Space Exploration

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IGAP: Interactive GA Peer to Peer

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Individual Visualization

Bedroom

Living RoomEating area

Bathroom

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Collaborative Visualization

User’s Individuals Peers’ Individuals

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Experimental Setup

• Objectives:– Create a floorplan for a 2 bedroom, 1 bathroom

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

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Experimental Setup

• Participants:– 8 women, 12 men

• Five groups of size four• Agenda

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

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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

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Hypothesis

• Is collaboration amongst peers sufficient to allow for the potential to produce creative content in designing?

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

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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

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Preliminary Conclusions

• Collaboration is not sufficient to make a clear distinction between individual and collaborative floorplans (?)

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Observations

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

• Simple graphic representation

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Observations: A New Hope

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

• Participants created only 1 collaborative floorplan and 1 individual floorplan

• Negative effect of viewing entire population– Not enough generations– Not enough sharing

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Conclusions

• Collaborative IGAs as a computational model of creative design

• Floorplans created collaboratively scored higher in “originality” and “revolutionary”– Scores in median range– Graphical representation

• We cannot deduce that collaboration is enough to introduce a creative potential

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Comments/Questions?

• quiroz@cse.unr.edu• www.cse.unr.edu/~quiroz

• Acknowledgements– This work was supported in part by contract number

N00014-0301-0104 from the Office of Naval Research and the National Science Foundation under Grant no. 0447416.