Evolutionary Computation (EC) mimics evolution processes to solve burdensome computational problems, like the design, optimization and reverse engineering of complex systems, and its effectiveness is tied to a proper formalization of the candidate solutions. Petri Net (PN) formalism is extensively exploited for the modeling, simulation and analysis of the structural and behavioral properties of complex systems. Here we introduce a novel evolutionary algorithm inspired by EC, the Evolutionary Petri Net (EPN), which is based on an extended class of PNs, called Resizable Petri Net (RPN), provided with two genetic operators: mutation and crossover. RPN includes the new concept of hidden places and transitions, that are used by the genetic operators for the optimization of PN-based models. We present a potential application of EPNs to face one of the most challenging problems in Systems Biology, the reverse engineering of biochemical reaction networks.
The foundation of Evolutionary Petri Nets / M.S. Nobile, D. Besozzi, P. Cazzaniga, G. Mauri. - 988:(2013), pp. 60-74. (Intervento presentato al 4th. convegno International Workshop on Biological Processes & Petri Nets (BioPPN 2013), a satellite event of PETRI NETS 2013 tenutosi a Milano nel 2013).
The foundation of Evolutionary Petri Nets
D. Besozzi;
2013
Abstract
Evolutionary Computation (EC) mimics evolution processes to solve burdensome computational problems, like the design, optimization and reverse engineering of complex systems, and its effectiveness is tied to a proper formalization of the candidate solutions. Petri Net (PN) formalism is extensively exploited for the modeling, simulation and analysis of the structural and behavioral properties of complex systems. Here we introduce a novel evolutionary algorithm inspired by EC, the Evolutionary Petri Net (EPN), which is based on an extended class of PNs, called Resizable Petri Net (RPN), provided with two genetic operators: mutation and crossover. RPN includes the new concept of hidden places and transitions, that are used by the genetic operators for the optimization of PN-based models. We present a potential application of EPNs to face one of the most challenging problems in Systems Biology, the reverse engineering of biochemical reaction networks.
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