ML Anthology

Learning Decision Rules for Scheduling Problems: A Classifier Hybrid Approach

Mike R. Hilliard, Gunar E. Liepins, Gita Rangarajan, Mark R. Palmer
ICML 1989 pp. 188-190
doi:10.1016/B978-1-55860-036-2.50053-9 /icml/1989/hilliard1989icml-learning/

Abstract

A series of experiments to learn general rules for simple job shop scheduling tasks suggest that the classifier system may work best as a component of a larger system. Preliminary results demonstrate the system's ability to learn binary decision rules as a component of a sorting routine.

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Cite

Text

Hilliard et al. "Learning Decision Rules for Scheduling Problems: A Classifier Hybrid Approach." International Conference on Machine Learning, 1989. doi:10.1016/B978-1-55860-036-2.50053-9

Markdown

[Hilliard et al. "Learning Decision Rules for Scheduling Problems: A Classifier Hybrid Approach." International Conference on Machine Learning, 1989.](https://mlanthology.org/icml/1989/hilliard1989icml-learning/) doi:10.1016/B978-1-55860-036-2.50053-9

BibTeX

@inproceedings{hilliard1989icml-learning,
  title     = {{Learning Decision Rules for Scheduling Problems: A Classifier Hybrid Approach}},
  author    = {Hilliard, Mike R. and Liepins, Gunar E. and Rangarajan, Gita and Palmer, Mark R.},
  booktitle = {International Conference on Machine Learning},
  year      = {1989},
  pages     = {188-190},
  doi       = {10.1016/B978-1-55860-036-2.50053-9},
  url       = {https://mlanthology.org/icml/1989/hilliard1989icml-learning/}
}