Optimizing creatively in multi-objective optimization
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Designers will always face the challenge of designing well-performing buildings using what are often conflicting and competing objectives. Early stage design decisions influence significantly the final performance of a building and designers are often unable to explore large numbers of design alternatives with respect to the performative criteria set for the project. This research outlines a optimization workflow' using a Multi-Objective Optimization (MOO) engine called Octopus that runs within Grasshopper3D, a parametric modeling tool, and simulation software DIVA for daylight factor analysis. The workflow utilizes a optimization tool' which allows the designer to explore, sort and filter solutions, and analyze both quantitatively and qualitatively the trade-offs of the resultant design solution space. It enables the designer to visually compare alternative solutions in a gallery and subsequently analyze trade-offs through a radar-based chart, parallel coordinate plot graphs and conditional domain searches. This feedback tools allows the designer to quickly and efficiently identify potential solutions for either design development or to select preferred solutions for further optimization, i.e. optimizing creatively'. A retrospective design case study, the De Rotterdam' building, is used to demonstrate the application of the tools. The workflow demonstrates the ability to reduce design latency and to allow for better understanding of design solutions. Additional research is needed to better understand the application of MOO in the early stages of design; and the further improvement of the creative optimization tools to accommodate the designer's need for a more dynamic and synergistic process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it