Micro-Differential Evolution: Diversity Enhancement and Comparative Study
Bibliographic record
Abstract
Evolutionary algorithms (EAs), such as the differential evolution (DE) algorithm, suffer\nfrom high computational time due to large population size and nature of evaluation, to\nmention two major reasons. The micro-EAs employ a very small population size, which\ncan converge to a reasonable solution quicker; while they are vulnerable to premature\nconvergence as well as high risk of stagnation. One approach to overcome the stagnation\nproblem is increasing the diversity of the population. In this thesis, a micro-differential\nevolution algorithm with vectorized random mutation factor (MDEVM) is proposed, which\nutilizes the small size population benefit while preventing stagnation through diversification\nof the population. The following contributions are conducted related to the micro-DE\n(MDE) algorithms in this thesis: providing Monte-Carlo-based simulations for the proposed\nvectorized random mutation factor (VRMF) method; proposing mutation schemes\nfor DE algorithm with populations sizes less than four; comprehensive comparative simulations\nand analysis on performance of the MDE algorithms over variant mutation schemes,\npopulation sizes, problem types (i.e. uni-modal, multi-modal, and composite), problem\ndimensionalities, mutation factor ranges, and population diversity analysis in stagnation\nand trapping in local optimum schemes. The comparative studies are conducted on the\n28 benchmark functions provided at the IEEE congress on evolutionary computation 2013\n(CEC-2013) and comprehensive analyses are provided. Experimental results demonstrate\nhigh performance and convergence speed of the proposed MDEVM algorithm over variant\ntypes of functions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".