HGLPSO: Hybrid Genetic Learning PSO and its Applications to Task Matching on Large-Scale Systems
Bibliographic record
Abstract
Matching tasks to be executed with proper resources is essential for improving the performance of grid systems. Assigning a set of tasks to a set of heterogeneous resources is challenging and becomes more complicated when the number of tasks and resources increases. This problem is known as thetask matching problem and is an NP-hard problem. Swarm Intelligence (SI) methods have been adopted as a solution to this problem. One such algorithm is particle swarm optimization (PSO); however, PSO tends to get stuck at local optima in such complex problems. This paper introduces a hybrid genetic learning PSO (HGLPSO) algorithm for the task matching problem in the grid environment. HGLPSO incorporates two genetic learning schemes to create candidate solutions (exemplars). Accordingly, the resulting exemplars possess the right balance of exploration and exploitation search abilities to direct the particles in the search space. The results demonstrate the effectiveness and efficiency of HGLPSO compared with other PSO variants in a heterogeneous grid environment.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".