Adaptation of an Autonomous Creative Evolutionary System for Real-World Design Application Based on Creative Cognition
Bibliographic record
Abstract
This paper describes the conceptual and implementation shift from a creative research-based evolutionary system to a real-world evolutionary system for professional designers. The initial system, DarwinsGaze, is a Creative Genetic Programing system based on creative cognition theories. It generated� artwork� that� 10,000’s� of� viewers� perceived as human-created art, during its successful run at peer-reviewed, solo shows at noted museums and art galleries. In an effort to improve the system for use with real-world designers, and with multi-person creativity in mind, we began working with a noted design firm exploring potential uses of our technology to support multivariant creative design iteration. This second generation system, titled Evolver, provides designers with fast, unique creative options that expand beyond their habitual selections that can be inserted/extracted from the system process at any time for modular use at varying stages of the creative design process. We describe both systems and the design decisions to adapt our research system, whose goal was to incorporate creativity automatically within its algorithms, to our second generation system, which attempts to take elements of human creativity theories and populate them as tools back into the process. We report on our study with the design firm on the adapted system’s� effectiveness.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".