Animal‐Behavior‐Inspired Algorithms in Analog Circuit Sizing Optimization
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
This chapter introduces various optimization algorithms inspired by both fauna and flora behaviors, including reproduction, foraging, preying, and the balance between exploration and exploitation. The optimization algorithms include particle swarm optimization, ant colony optimization, bat algorithm, firefly algorithm (FA), cuckoo search (CS) and flower pollination algorithm. Most of these algorithms exhibit underlying characteristics of exploration and exploitation processes based on individual or group experiences in choosing the best individuals or strategies to reach their targets. This analogy to integrated circuit designs highlights their potential application, further clarified through a case study of implementing the CS algorithm in BGR circuit design. FA is designed to solve global optimization problems, where each firefly individual in the population interacts with others based on their brightness. The chapter explores the method of employing various renowned metaheuristic optimization algorithms aimed at maximizing the power supply rejection ratio parameter of the BGR circuit.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 it