Gaggle: Genetic Algorithms on the GPU using PyTorch
Bibliographic record
Abstract
PyTorch has profoundly impacted the machine learning (ML) community by allowing researchers of all backgrounds to train models efficiently. While PyTorch is the de facto standard in ML, the evolutionary algorithms (EA) community instead relies on many different libraries, each with low adoption in practice. In an effort to provide a standardized library for EA, packages like LEAP and PyGAD have been developed. However, these libraries fall short in either scalability or usability. In particular, neither of these packages offers efficient support for neuroevolutionary tasks. We argue that the best way to develop a PyTorch-like library for EAs is to build on the already solid foundation of PyTorch itself. We present Gaggle, an efficient PyTorch-based EA library that better supports GPU-based tasks like neuroevolution while maintaining the efficiency of CPU-based problems. We evaluate Gaggle on various problems and find statistically significant improvements in runtime over prior work on problems like training neural networks. In addition to efficiency, Gaggle provides a simple single-line interface making it accessible to beginners and a more customizable research interface with detailed configuration files to better support the EA research community.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.029 | 0.011 |
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".