Minimal Initial Marking Estimation Based on Genetic Algorithm
When considering label sequences,computing the minimal initial marking set of a labeled Petri net with unobservable transitions is a complex task.Existing methods to address this issue come with various limitations.We employ a genetic algorithm-based approach to estimate the minimal initial markings.Due to the potential existence of multiple initial markings(often infinite),the focus lies in obtaining a minimal initial marking set in the Petri net,satisfying the following conditions:the initial markings allow at least one firing se-quence consistent with the observed label sequences and network structure;the initial markings have the minimum total token count(i.e.,the lowest sum of tokens over all places),and for each observed label,allowance is made for at most one unobservable transition firing before each observable transition firing.Given that estimating the minimal initial markings falls within the NP-hard category,the a-doption of such algorithms is reasonable.We demonstrate the effectiveness of the proposed method through practical experiments.Compared with the existing algorithm,the proposed algorithm can obtain the minimum initial marking estimate with a lower computational cost.
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