Evolutionary Algorithm and Its Application in Artificial Immune System
Why this work is in the frame
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Bibliographic record
Abstract
Analyses were made on the basic principles of evolutionary algorithm, evolution strategies and evolution programming. Considering the superiority of evolutionary algorithm in intellectual computing, we analyze a typical optimizing algorithm for artificial immune system (AIS). Combining evolutionary algorithm and artificial immunity, we present an immune intrusion analysis scheme based on statistical analyzing model. The scheme introduces the prominent characteristics of evolutionary algorithm, such as parallel operating, successive optimizing into intrusion parameter selecting, data collecting and intrusion analyzing, thus it effectively improves the applicableness of immune IDS. The scheme avoids the security threats and weakness arising from the transfer of immune pathology metaphor mechanisms into AIS. As a comparison with other artificial immune schemes, we also provide an application case of the immune analyzing scheme in intrusion detecting and dealing, the comparison further justifies the scheme's adaptability, stability, robustness and parallel operating regarding its application in software and hardware circumstances.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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