Evolving Petri Nets with a Genetic Algorithm

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  author =       "Holger Mauch",
  title =        "Evolving {Petri} Nets with a Genetic Algorithm",
  booktitle =    "Genetic and Evolutionary Computation -- GECCO-2003",
  editor =       "E. Cant{\'u}-Paz and J. A. Foster and K. Deb and 
                 D. Davis and R. Roy and U.-M. O'Reilly and H.-G. Beyer and 
                 R. Standish and G. Kendall and S. Wilson and 
                 M. Harman and J. Wegener and D. Dasgupta and M. A. Potter and 
                 A. C. Schultz and K. Dowsland and N. Jonoska and 
                 J. Miller",
  year =         "2003",
  pages =        "1810--1811",
  address =      "Chicago",
  publisher_address = "Berlin",
  month =        "12-16 " # jul,
  volume =       "2724",
  series =       "LNCS",
  ISBN =         "3-540-40603-4",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, poster",
  DOI =          "doi:10.1007/3-540-45110-2_76",
  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
  notes =        "GECCO-2003. A joint meeting of the twelfth
                 International Conference on Genetic Algorithms
                 (ICGA-2003) and the eighth Annual Genetic Programming
                 Conference (GP-2003)",

Genetic Programming entries for Holger Mauch