Automatically generated object-oriented genetic programs to optimize adaptive job shop control and scheduling system.
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
The requirements for Production Planning and Control (PP&C) System have fundamentally changed during last years. The increasingly complex production processes and constantly changing production environment require the system able to be agile, flexible and adaptable to the changing situations from markets, customers, new technology, environment, and so on. Fraunhofer Institute of Production and Automation (IPA), Stuttgart. Germany created an experimentation environment of software agent, event-oriented simulation and evolutionary strategies, to examine adaptive approach for the PP&C system. The project of Agent Learning Adaptive Network (ALAN), Fraunhofer---IPA proposed, focused on the Job Shop Control level to explore the new order management paradigm applicable to small and medium sized enterprises (SMEs). Object-oriented programs automatically generated by Genetic Programming are expected to automatically co-ordinate between multiple intelligent agents to reach system's global targets. This research extends genetic programming beyond its current generation of functional and procedural programs to the generation of object-oriented programs. Successful achievement of this goal will represent a significant advance in the practice of genetic programming in Object-Orientation. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2001 .Y83. Source: Masters Abstracts International, Volume: 40-06, page: 1561. Adviser: Arunita Jaekel. Thesis (M.Sc.)--University of Windsor (Canada), 2001.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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