A Hierarchical Surrogate-Assisted Differential Evolution With Core Space Localization
Bibliographic record
Abstract
Surrogate-assisted evolutionary algorithms (SAEAs) are extensively used to tackle expensive optimization problems (EOPs). The integration of surrogate-based global and local search is a prevalent hierarchical SAEA framework, which can effectively balance exploration and exploitation capabilities. However, it still faces challenges when tackling high-dimensional EOPs (HEOPs) owing to the curse of dimensionality. In this article, we propose a hierarchical surrogate-assisted differential evolution with core space localization (HSADE-CS) to solve HEOPs. Its contributions are listed as follows: 1) a top-promising sampling strategy is introduced in the global search to mitigate the challenges posed by the uncertainty in the performance of the surrogate model; 2) a core space localization (CSL) method is proposed to identify a high-potential space within the local promising region, enhancing the effectiveness of local search; and 3) a fitness-independent adaptive parameter control method based on the Minkowski distance is developed within the differential evolution (DE) optimizer to improve the performance of surrogate model-driven local search. The performance of HSADE-CS has been validated on numerous benchmark problems from the commonly used expensive optimization benchmark suite, as well as the CEC2014 and CEC2017 benchmark suites, with problem dimensions up to 500. It has also been tested on a real-world problem, i.e., circular antenna array design optimization. Experimental results demonstrate that HSADE-CS is highly competitive compared to the state-of-the-art SAEAs.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".