Optimization of Electromagnetic Devices Using Artificial Immune Systems
Bibliographic record
Abstract
Optimization algorithms based on principles inspired from the immune system are capable of achieving an arbitrary set of optima, including the global solution. These algorithms differ in the way they implement the encoding, cloning, maturation and replacement steps, which are the basic ingredients of optimization algorithms based on artificial immune systems. This paper presents the Distributed Clonal Selection Algorithm (DCSA), which employs different probability dis- tributions for the maturation step. The performance of the DCSA is compared with the Real-Coded Clonal Selection Algorithm (RCSA) and the B-Cell Algorithm (BCA) in the design of a waveguide and in the TEAM benchmark problem 22. The DCSA presents better conver- gence speed, in terms of number of evaluations, being 8% faster than the RCSA and78% faster than the BCA, for the minimization of the re- turn loss of a 3D waveguide impedance transformer. In the 8D TEAM problem, the DCSA and RCSA respect the energy constraint with a maximum error of 2.2% while the BCA presents high violations. Re- garding these methods, the DCSA achieves better values for the stray magnetic flux density.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".