Enhancing SHADE and L-SHADE Algorithms Using Ordered Mutation
Why this work is in the frame
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Bibliographic record
Abstract
Differential Evolution (DE) algorithm is an efficient population-based metaheuristic algorithm which has shown satisfactory performance in solving complex real-world optimization problems. A Success-History Based Parameter Adaptation for Differential Evolution (SHADE) is a well-established variant of DE algorithm which employs a historical performance of the successful control parameters. L-SHADE algorithm extends SHADE with a linear population size reduction strategy. The SHADE and L-SHADE algorithms employ current-to-pbest/1 strategy for evolution, whilst the order of candidate solutions is not considered in their schemes. This paper proposes an ordering strategy for SHADE and L-SHADE algorithms which has shown a satisfactory influence on the performance of both algorithms. In the first direction, we propose current-to-3order/1 strategy for SHADE algorithm, which is based on ordering three candidate solutions. In the second direction, L-SHADE algorithm is improved based on ordering two candidate solutions, called current-to-pbest-2order/1. The proposed strategies can improve the performance of SHADE and L-SHADE algorithms without adding any extra significant computational cost. The proposed strategy is evaluated on CEC-2017 benchmark functions and with dimensions 30, 50, and 100. Our experimental results clearly verify the effectiveness of the proposed strategies.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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