A case study on target cost estimation using a genetic algorithm and a back-propagation based neural network
Bibliographic record
Abstract
Establishing the target cost of new products has always been difficult, as only a few attributes of the product as usually known. In these circumstances, parametric methods are commonly used by using a predetermined cost function where the considered parameters are evaluated from historical data. In contrast to the regression or parametric models, neural networks, in are non-parametric which attempt to fit curves to predict the cost without being provided a predetermined function. In this paper, the above mentioned property of neural networks is used to investigate their applicability for cost estimation of a certain major aircraft component. This empirical study is conducted in collaboration with a major aerospace company located in Montreal, Canada. Two neural network models, one trained by the gradient decent algorithm and the other by genetic algorithm, are considered and contrasted to one another. The study, using historical data, shows that the neural network model trained by genetic algorithm outperforms the model trained by back-propagation as it fits well both in the training and validation data sets.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.003 | 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".