Surrogate-Assisted Differential Evolution Using Knowledge-Transfer-Based Sampling for Expensive Optimization Problems
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
To sufficiently reuse the knowledge from previous optimization efforts, a surrogate-assisted differential evolution using knowledge-transfer-based sampling (denoted as SADE-KTS) method is proposed for solving expensive black-box optimization problems. In SADE-KTS, a novel knowledge-transfer-based sampling method is integrated with the differential evolution framework to generate promising initial sample points. In this way, a least-squares support vector machine classifier is constructed based on the prior optimization knowledge database to calibrate the initial sample points adaptively, which improves the exploration performance via transferring the existed optimization efforts to the current optimization task. Moreover, the radial basis function and kriging surrogates are employed to replace the expensive simulation models for evolutionary operations, where the tailored differential evolution operators are cooperated with the sequential quadratic programming optimizer to lead the search to the global optimum efficiently. A number of numerical benchmarks are tested to illustrate the optimization capacity of SADE-KTS compared with several competitive optimization algorithms. Finally, SADE-KTS is applied to an airfoil aerodynamic knowledge-based optimization problem considering the existed optimization knowledge, which demonstrates the practicality and effectiveness of the proposed SADE-KTS in engineering practices.
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.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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