A Comparative Study Of Representation And Encodings For Building Shape Optimization With Genetic Algorithms
Bibliographic record
Abstract
This paper presents a methodology to optimize the building footprint represented by a multisided polygon. Two geometrical representations for a polygon are considered. The first representation uses edge lengths and edge angles to define a polygon while the second representation uses edge lengths and edge bearings. These two representations are discussed with emphasis on their potential problems in binary coding for genetic algorithms: epistasis and encoding isomorphism. Epistasis implies the gene interaction when one gene pair masks or modifies the expression of other gene pairs. Encoding isomorphism means that chromosomes with different binary strings may map to the same solution in the design space. The two alternative representation methods are compared in terms of their impacts on computational effectiveness and efficiency. A problem is formulated to facilitate the comparison, where a pentagon-shaped typical floor of an office building is optimized with respect to life-cycle cost and life-cycle environmental impact. It is found that epistasis has a large impact on the performance of the multi-objective genetic algorithm while encoding isomorphism is not a problem.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".