A Knowledge-Migration-Based Multi-Population Cultural Algorithm to Solve Job Shop Scheduling.
Bibliographic record
Abstract
In this article, a multipopulation Cultural Algorithm (MP-CA) is proposed to solve Job Shop Scheduling Problems (JSSP). The idea of using multiple populations in a Cultural Algorithm is implemented for the first time in JSSP. The proposed method divides the whole population into a number of sub-populations. On each sub-population, a local CA is applied which includes its own population space as well as belief space. The local CAs use Evolutionary Programming (EP) to evolve their populations, and moreover they incorporate a local search approach to speed up their convergence rates. The local CAs communicate with each other using knowledge migration which is a novel concept in CA. The proposed method extracts two types of knowledge including normative and topographic knowledge and uses the extracted knowledge to guide the evolutionary process to generate better solutions. The MPCA is evaluated using a well-known benchmark. The results show that the MP-CA outperforms some of the existing methods by offering better solutions as well as better convergence rates, and produces competitive solutions when compared to the state-of-the-art methods used to deal with JSSPs.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".