Analyzing effects of ordering vectors in mutation schemes on performance of Differential Evolution
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Bibliographic record
Abstract
Differential Evolution (DE) is a simple powerful evolutionary algorithm for solving global continuous optimization problems. The especial characteristic of DE algorithm is calculating a weighted difference vector of two random candidate solutions in the population to generate the new promising candidate solutions. A major operation of the DE algorithm is the mutation which can affect its performance. The main goal of this study is investigating the influence of ordering vectors on various mutation schemes. We design some Monte-Carlo based simulations to analyze several mutation schemes by calculating the probability of closeness of a new trial solutions to a random optimal solution. These simulations indicate that mutation schemes can enhance the performance of the DE algorithm which they consider right ordering of the vectors in their mutation operators. Also, we introduce a new mutation scheme which considers in ordering vectors in the mutation scheme. We benchmark the modified DE algorithm with the ordered mutation scheme (DE/order) on CEC-2014 test functions with three dimensions 30, 50, and 100. Simulation results confirm that DE/order obtains a promising performance on the majority of the test functions on all mentioned dimensions.
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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 | 0.000 | 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 | 0.000 |
| 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