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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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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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?
• [email protected]• 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.