Novel genetic algorithm for the multiplexed computer-generated hologram with polygonal apertures
Bibliographic record
Abstract
A novel genetic algorithm (GA) with a Lamarckian search is proposed for the design of the multiplexed computergenerated hologram (MCGH) with polygonal apertures. The Fraunhofer image of the new MCGH is computed by coherent addition of the subhologram subimages. The subimages are obtained by multiplying the fast Fourier transforms of the subhologram transmittance distributions by layout coefficients computed with the Abbe transform. The division into polygonal apertures is the same for all cells, and defines the polygonal layout of the cells. In our preceding designs of the MCGH with polygonal apertures, only the subhologram transmittances, but not the polygonal layout of the cells, were optimized with our iterative subhologram design algorithm (ISDA). In this paper, we optimize for the first time the polygonal layout of the MCGH cells with a novel GA. For fabrication by e-beam lithography, each cell is composed of a number of stripes. Each stripe is divided into some trapezoidal apertures, which can (i) take a number of different shapes and (ii) belong to a number of different subholograms. The number of possible polygonal layouts for the cells therefore is huge and equal to 264 = 1.85 × 1019 in the case of a MCGH with five subholograms. Each possible layout is coded as a chromosome of bits. Our novel GA performs crossovers and mutations. However, differently from the classical GA, our new GA also uses a Lamarckian search based on a gradient descent, and rapidly determines the optimal polygonal layout for the MCGH cells.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".