Intelligent optimization algorithm for global convergence of non-convex functions based on improved fuzzy algorithm
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 | 0.000 |
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