Three-Step Parameters Tuning Model for Time-Constrained Genetic Algorithms
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Bibliographic record
Abstract
In this paper a three-step parameters tuning model for time-constrained Genetic Algorithms (GAs) was presented. The first step involved modeling the objective function using multiple regression model where the fitness value was the response variable and the GA parameters were the regressors. The second step involved constraint modeling using the objective function found in the first step and using the upper and lower limits of the GA parameters along with an upper limit on the execution time as constraints. The third step involved optimizing the constraint model found in the second step using a suitable deterministic optimization method to determine the optimal GA parameters taking into consideration four aspects that affect the GA performance. These aspects were: the problem under consideration, the GA parameters used, the execution time, and the power of the computer used.The validation of this model was demonstrated using two capacitated lot sizing problems. The model was able to predict the fitness values and the optimal parameters of the GA for these problems to a high degree of precision. Moreover, the results showed that tuning the GA parameters using multiple regression along with a suitable deterministic optimization method was an effective and robust method that enhanced the performance of the GA. The statistical analysis showed that in order to do a proper tuning for a certain GA, the designer of the GA must take into consideration not only the type of problem but also the size of the problem, the allowable execution time, and the hardware used in executing the GA. Furthermore, the results agreed with the "No Free Lunch" theorem.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| 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