Multi-combinative strategy to avoid premature convergence in genetically-generated fuzzy knowledge bases
Bibliographic record
Abstract
A growing number of industrial fields is concerned by complex and multi-objective problems. For this kind of problems, optimal decision making is critical. Decision support systems using fuzzy logic are often used to deal with complex and large decision making problems. However the main drawback is the need of an expert to manually construct the knowledge base. The use of genetic algorithms proved to be an effective way to solve this problem. Genetic algorithms model the life evolution strategy using the Darwin theory. A main problem in genetic algorithms is the premature convergence, and the last enhancements in order to solve this problem include new multi-combinative reproduction techniques. There are two principal ways to perform multi-combinative reproduction within a genetic algorithm, namely the Multi-parent Recombination, Multiple Crossover on Multiple Parents (MCMP); and the Multiple Crossovers Per Couple (MCPC). Both techniques try to take the most of the genetic information contained in the parents. This paper explores the possibility to decrease premature convergence in a real/binary like coded genetic algorithm (RBCGA) used in automatic generation of fuzzy knowledge bases (FKBs). The RBCGA uses several crossover mechanisms applied to the same couple of parents. The crossovers are also combined in different ways creating a multiple offspring from the same parent genes. The large family concept and the variation of the crossovers should introduce diversity and variation in otherwise prematurely converged populations and hence, keeping the search process active.
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.
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 | 0.003 | 0.006 |
| 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".