Intelligent optimization algorithm for global convergence of non-convex functions based on improved fuzzy algorithm
Bibliographic record
Abstract
The traditional global convergence optimization algorithm is prone to premature convergence and slow convergence in the face of complex non-convex function. To this end, a new intelligent optimization algorithm based on improved fuzzy algorithm for global convergence of non-convex function is proposed. The general model of the optimal problem is designed and the general model of non-convex function is established. The genetic algorithm is used to optimize the non-convex function, and the global convergence of the current non-convex function is analyzed. It is found that the global convergence of non-convex function is actually based on the optimization of crossover probability and mutation probability to decide the convergence of genetic algorithm, so as to improve the global convergence. A fuzzy controller is designed, which determines the input and output variables and their membership functions, establishes fuzzy rules and anti-fuzzing process to control the crossover rate. The fuzzy control of mutation rate is similar to the crossover rate, but the difference is that the new fuzzy control rule is needed. The experimental results show that the proposed algorithm can effectively optimize the global convergence of non-convex function.
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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) | 0.002 | 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".