FIR filter design using Multiobjective Artificial Bee Colony algorithm
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, general FIR filters are designed using multiobjective Artificial Bee Colony algorithm. Spherical pruning (SP) and physical programming (PP) techniques are combined together in the implementation of multiobjective Artificial Bee Colony algorithm. Physical programming converts the design objectives into an intuitive language and spherical pruning maintains diversity in the Pareto front. The design of general FIR filters require simultaneous optimization of magnitude and group delay errors and therefore can be formulated as a Multiobjective Optimization (MOO) problem. All the non-dominated solutions of the general FIR design problem can be approximated into a Pareto front. Numerical results show that, multiobjective Artificial Bee Colony algorithm can achieve lower passband, stopband, group delay errors when compared to those of spherical pruning Multiobjective Differential Evolution (spMODE-II).
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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