A Review of Benchmark and Test Functions for Global Optimization Algorithms and Metaheuristics
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
ABSTRACT Benchmarking in optimization is a critical step in evaluating the performance, robustness, and scalability of machine learning algorithms and metaheuristics. While trends in benchmark design continue to evolve, synthetic functions remain vital for fundamental stress tests and theoretical evaluations. As several benchmark and test functions have been developed and derived over the past decades, little attention has been given to classifying such test functions and the rationale behind their usage. From this lens, this paper reviews and categorizes a broad range of functions often employed in assessing optimizers and metaheuristics. More specifically, we classify test functions based on modality, dimensionality, separability, smoothness, constraints, and noise characteristics to offer a broad view that aids in selecting appropriate benchmarks for various algorithmic challenges. Then, this review also discusses in detail the 25 most commonly used functions in the open literature and proposes two new, highly dimensional, dynamic, and challenging functions that could be used for testing new algorithms. Finally, this review identifies gaps in current benchmarking practices and directions for future research, as well as suggests best practices and guidelines.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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