A Systematic Assessment of Genetic Algorithm (GA) in Optimizing Machine Learning Model: A Case Study from Building Science
Why this work is in the frame
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Bibliographic record
Abstract
Machine learning (ML) algorithms are techniques that allow computers to learn from the data without being explicitly programmed. ML techniques consist of hyperparameters that typically influence prediction accuracy, hence requiring tuning. In this study, we systematically evaluate the performance of the genetic algorithm (GA) technique in tuning ML hyperparameters compared to three other common tuning techniques i.e. grid search (GS), random search (RS), and bayesian optimization (BO). While previous studies explored the potential of metaheuristics techniques such as GA in tuning ML models, a systematic comparison with other commonly mentioned techniques is currently lacking. Results indicate that GA slightly outperformed other methods in terms of optimality due to its ability to pick any continuous value within the range. However, apart from GS which took the longest, it was observed that GA is quite a time inefficient compared to RS and BO which were able to find a solution close to the GA within a shorter time (GA – 149 minutes, RS – 88 minutes, BO – 105 minutes, GS – 756 minutes).
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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