On Using Genetic Algorithm Optimized Activation Functions to Increase Neural Network Accuracy
Bibliographic record
Abstract
Neural Networks have been successfully applied to many problems throughout aerospace engineering. Such networks learn a training set by adjusting the weights assigned between connected pairs of neurons. Historically, optimization of the Neural Network’s input variables has reduced the differences between the predictions and the training data. In order to further improve the accuracy of the Neural Network predictions, a new approach is proposed that includes optimization of the activation function(s) used in the network’s neurons. In this approach, third-order Bezier Curves are used to define the activation function. The optimizer adjusts the control points for these curves to adapt the activation function(s) to the specific problem under study. Concurrently, optimization of the weighting values is being performed. A Genetic Algorithm optimizer is used to determine the superior Bezier Curve control point locations. The resulting Neural Networks demonstrate superior performance, measured by the total accuracy of the predictions, over those using traditional (i.e. non-optimized) activation functions, with error reductions from 23%-76% for three test cases.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".