An evolutionary self-learning methodology: Some preliminary results from a case study (original) (raw)
Abstract
The self-learning methodology is a four-step technique; (1): data from a process is collected, (2): the collected data is used to infer a model of the process, (3): this model is then intelligently interrogated by the computer in order to ‘discover’ a process improvement opportunity, (4): as new data become available from the process Step 2 of the methodology may be repeated in order to build a process model which is more realistic.
This self-learning methodology is almost solely driven by information extracted from process data. Hence the requirement for process domain expertise is minimal and the self-learning methodology is consequently relatively generic.
This paper provides a description of the self-learning methodology. A four-fold improvement in the tensile tolerance of a popular steel product is shown to be possible in laboratory experiments.
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References
- Barber, C.B., Dobkin, D.P. and Huhdanpaa, H.T., 1996, The Quickhull Algorithm for Convex Hulls, ACM Transactions on Mathematical Software, 22, 4, pp469–483
Google Scholar - Barber, C.B., Dobkin, D.P. and Huhdanpaa, H.T., 1993, The Quickhull Algorithm for Convex Hulls, Technical Report GCG53, The Geometry Center, Minneapolis, MN, ftp://geom.umn.edu/pub/documents/preprints /GCG53
Google Scholar - Bowles, A., 1996, Intelligent Manufacturing Systems Project Discussion Document, Technical Note BHPR/CP/N/G/043, BHP Research
Google Scholar - Fogel, D.B., 1994, Evolutionary programming: an introduction and some current directions, Stat. Comput., 4, pp113–130
Google Scholar - Goldberg, D.E., 1989, Genetic Algorithms in Search, Optimizsation, and Machine Learning, Reading, MA: Addison-Wesley
Google Scholar - Lippman, R.P., 1997, An Introduction to computing with Neural Networks, IEEE ASSP Magazine, April, pp4–22
Google Scholar - Quinlan, J.R., 1992, Learning With Continuous Classes, Proceedings AI'92, Singapore: World Scientific, pp343–348
Google Scholar - Quinlan, J.R., 1993, Combining Instance-Based And Model-Based Learning, Proceedings ML'93, San Mateo: CA: Morgan Kaufmann
Google Scholar - Rumelhart, D.E., Hinton, G.E. and William, R.G., 1986, Learning Representations by Back-Propagating Errors, Nature, 323, pp533–536
Google Scholar - Weigend, A.S., Rumelhart, D.E. and Huberman, B.A., 1991, Generalization By Weight-Elimination With Application to Forecasting, In R.P. Lippman, J. Moody, and D.S. Touretzky, (eds.) Advances in Neural Information Processing Systems 3 (NIPS90). Morgan Kaufmann
Google Scholar
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Authors and Affiliations
- BHP Research, 245 Wellington Road, 3170, Mulgrave Vic, Australia
Sharad Thacore
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V. W. Porto N. Saravanan D. Waagen A. E. Eiben
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© 1998 Springer-Verlag Berlin Heidelberg
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Thacore, S. (1998). An evolutionary self-learning methodology: Some preliminary results from a case study. In: Porto, V.W., Saravanan, N., Waagen, D., Eiben, A.E. (eds) Evolutionary Programming VII. EP 1998. Lecture Notes in Computer Science, vol 1447. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0040791
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- DOI: https://doi.org/10.1007/BFb0040791
- Published: 10 December 2005
- Publisher Name: Springer, Berlin, Heidelberg
- Print ISBN: 978-3-540-64891-8
- Online ISBN: 978-3-540-68515-9
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