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Production system identification with genetic programming

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conference contribution
posted on 23.05.2018, 10:49 by Peter Denno, Charles DickersonCharles Dickerson, Jennifer HardingJennifer Harding
Modern system-identification methodologies use artificial neural nets, integer linear programming, genetic algorithms, and swarm intelligence to discover system models. Pairing genetic programming, a variation of genetic algorithms, with Petri nets seems to offer an attractive, alternative means to discover system behaviour and structure. Yet to date, very little work has examined this pairing of technologies. Petri nets provide a grey-box model of the system, which is useful for verifying system behaviour and interpreting the meaning of operational data. Genetic programming promises a simple yet robust tool to search the space of candidate systems. Genetic programming is inherently highly parallel. This paper describes early experiences with genetic programming of Petri nets to discover the best interpretation of operational data. The systems studied are serial production lines with buffers.

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Published in

International Conference on Manufacturing Research

Citation

DENNO, P., DICKERSON, C.E. and HARDING, J.A., 2017. Production system identification with genetic programming. IN: Gao, J., El Souri, M. and Keates, C. (eds). Advances in Manufacturing Technology XXXI, Proceedings of the 15th International Conference on Manufacturing Research (ICMR 2017), London, UK, 5-7 September 2017, pp.227-232.

Publisher

IOS Press

Version

AM (Accepted Manuscript)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Publication date

2017

Notes

The definitive published version of this conference paper is available at IOS Press at https://doi.org/10.3233/978-1-61499-792-4-227.

ISBN

9781614997917;9781614997924

Language

en

Location

London