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Evolving Petri Nets with a Genetic Algorithm

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Genetic and Evolutionary Computation — GECCO 2003 (GECCO 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2724))

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Abstract

In evolutionary computation many different representations (“genomes”) have been suggested as the underlying data structures, upon which the genetic operators act. Among the most prominent examples are the evolution of binary strings, real-valued vectors, permutations, finite automata, and parse trees. In this paper the use of place-transition nets, a low-level Petri net (PN) class [1],[2], as the structures that undergo evolution is examined. We call this approach “Petri Net Evolution” (PNE). Structurally, Petri nets can be considered as specialized bipartite graphs. In their extended version (adding inhibitor arcs) PNs are as powerful as Turing machines. PNE is therefore a form of Genetic Programming (GP). Preliminary results obtained by evolving variable-size place-transition nets show the success of this approach when applied to the problem areas of boolean function learning and classification.

This research was supported in part by DARPA grant NBCH1020004.

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References

  1. Reisig, W.: Petri Nets: an Introduction. Springer-Verlag, Berlin, Germany (1985)

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  2. Murata, T.: Petri nets: Properties, analysis and applications. Proceedings of the IEEE 77 (1989) 541–580

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  3. Mitchell, T.M.: Machine Learning. WCB/McGraw-Hill, Boston, MA (1997)

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Author information

Authors and Affiliations

  1. Dept. of Information and Computer Science, University of Hawaii at Manoa, 1680 East-West Road, Honolulu, HI, 96822

    Holger Mauch

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  1. Holger Mauch
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Editor information

Editors and Affiliations

  1. Center for Applied Scientific Computing (CASC), Lawrence Livermore National Laboratory, 7000 East Avenue, L-561, Livermore, CA, 94550, USA

    Erick Cantú-Paz

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© 2003 Springer-Verlag Berlin Heidelberg

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Mauch, H. (2003). Evolving Petri Nets with a Genetic Algorithm. In: Cantú-Paz, E., et al. Genetic and Evolutionary Computation — GECCO 2003. GECCO 2003. Lecture Notes in Computer Science, vol 2724. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45110-2_76

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  • DOI: https://doi.org/10.1007/3-540-45110-2_76

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40603-7

  • Online ISBN: 978-3-540-45110-5

  • eBook Packages: Springer Book Archive

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