Incorporating a Genetic Algorithm to improve the performance of Variable Neighborhood Search
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Variable Neighborhood Search (VNS) is an efficient metaheuristics in solving optimization problems. Although VNS has been successfully applied on various problem domains, it suffers from its inefficient search exploration. To improve this limitation, VNS can be joined with a population-based search to benefit from its search exploration. In this article, a Memetic Algorithm (MA) is proposed which is based on a Genetic Algorithm (GA) incorporating VNS as a local search method. To evaluate the proposed method, it has been applied on the classical Job Shop Scheduling Problem (JSSP) as a well-known optimization problem. The experimental results show that the proposed MA outperforms the VNS method. Furthermore, compared to the state-of-the-art Evolutionary Algorithms (EAs) proposed to solve JSSP, the proposed method offers competitive solutions.
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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