Production System Identification with Genetic Programming

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.

[1]  Mariagrazia Dotoli,et al.  Real time identification of discrete event systems by Petri Nets , 2007 .

[2]  Giancarlo Mauri,et al.  The foundation of Evolutionary Petri Nets , 2013, BioPPN@Petri Nets.

[3]  Wil M. P. van der Aalst,et al.  Discovering simulation models , 2009, Inf. Syst..

[4]  Riccardo Poli,et al.  A Field Guide to Genetic Programming , 2008 .

[5]  Francesco Basile,et al.  Identification of Time Petri Net Models , 2017, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[6]  Lorenzo Tiacci,et al.  Coupling a genetic algorithm approach and a discrete event simulator to design mixed-model un-paced assembly lines with parallel workstations and stochastic task times , 2015 .

[7]  Bernard Riera,et al.  Design and Identification of Stochastic and Deterministic Stochastic Petri Nets , 2012, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[8]  Li Fu,et al.  The Research Survey of System Identification Method , 2013, 2013 5th International Conference on Intelligent Human-Machine Systems and Cybernetics.

[9]  Dominic P. Searson.  GPTIPS 2: An Open-Source Software Platform for Symbolic Data Mining , 2014, Handbook of Genetic Programming Applications.

[10]  C. Seatzu,et al.  Fault diagnosis and identification of discrete event systems using Petri nets , 2008, 2008 9th International Workshop on Discrete Event Systems.

[11]  Klaus-Dieter Thoben,et al.  Machine learning in manufacturing: advantages, challenges, and applications , 2016 .