GSR: A New Genetic Algorithm for Improving Source and Channel Estimates
Bibliographic record
Abstract
In this paper, we introduce a new genetic algorithm, which allows us to refine the estimates of information source symbols and channel estimates obtained by any identification algorithm. Instead of searching the entire space, the proposed algorithm searches for the refined estimates in the subspaces near the initial estimate. Creation of initial guesses by using problem specific information and new specially tailored nonblind genetic operators, based on the ideas from schema theory, for realizing the proposed approach are described. The new genetic source symbol refinement (GSR) algorithm is tested to cope with rapidly varying finite-impulse response channels with additive noise model. The method is capable of offering fast convergence with directed search ability and exhibits a unique feature of automatic adjustment in the number of cost function evaluations with the varying signal-to-noise ratio (SNR). Computational results show that the GSR can achieve the bit-error-rate performance near to the simulated annealing bound. As compared with recent sophisticated alternatives for the problem, the GSR performance is superior over a wide range of SNR, with reduced complexity
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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 | 0.001 |
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 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".