A Systematic Assessment of Genetic Algorithm (GA) in Optimizing Machine Learning Model: A Case Study from Building Science
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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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 | 0.004 | 0.010 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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 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".