Bibliographic record
Abstract
In this paper we describe nonlinear system modeling and design using genetic programming (GP). In order to demonstrate the ability of GP to design complex systems, we first present a novel scheme called improved least squares genetic program (ILS-GP) that attempts to reconstruct the functional form of a nonlinear dynamical system from its noisy time series measurements. ILS-GP augments the structural search ability of GP with a novel parameter estimation scheme called improved least squares designed specifically to eliminate bias in parameter estimates of the nonlinear dynamical system in the presence of measurement noise. We use different test chaotic systems and real-life radar sea scattered signals to demonstrate the effectiveness of the ILS-GP approach in reconstructing nonlinear systems. Having shown the ability of GP to reconstruct complex systems from their time series measurements, we apply GP to the reverse problem of constructing optimal systems for generating specific sequences called spreading codes in CDMA communications. Using different approaches including correlation properties and the bit error rate, we use the proposed GP approach to design chaotic piecewise maps that generate optimal spreading codes in complicated communication environments such as multi-path. Based on computer simulations, we have shown improved performance of the GP-generated maps when compared to the other approaches including the standard Gold code.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".