2010
DOI: 10.1007/978-3-642-11467-0_10
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Abstract: Summary. In this paper, a computational complexity theory within the framework of Membrane Computing is introduced. Polynomial complexity classes associated with different models of cell-like and tissue-like membrane systems are defined and the most relevant results obtained so far are presented. Many attractive characterizations of P = NP conjecture within the framework of a bio-inspired and non-conventional computing model are deduced.

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Cited by 27 publications
(16 citation statements)
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References 26 publications
(35 reference statements)
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“…To solve decision problems (i.e., decide languages over an alphabet ), we use families of recognizer P systems = { x ∶ x ∈ ⋆ } , as commonly done in the framework of Membrane Computing when one considers semiuniform solutions ( [30]). Given an input x, we consider a specific P system x that decides the membership of x in the language L ⊆ ⋆ by accepting or rejecting.…”
Section: Definition 1 a P System With Active Membranes Having Ini-mentioning
confidence: 99%
“…To solve decision problems (i.e., decide languages over an alphabet ), we use families of recognizer P systems = { x ∶ x ∈ ⋆ } , as commonly done in the framework of Membrane Computing when one considers semiuniform solutions ( [30]). Given an input x, we consider a specific P system x that decides the membership of x in the language L ⊆ ⋆ by accepting or rejecting.…”
Section: Definition 1 a P System With Active Membranes Having Ini-mentioning
confidence: 99%
“…Analogously, a family = { (n) ∶ n ∈ ℕ} of recognizer P systems with input membrane, solving X in a uniform way, has been defined. The reader is referred to [43] for more details.…”
Section: Definition 3 ([43]mentioning
confidence: 99%
“…Some of these models (see [5,8], for instance) were shown to be complete by simulating, in a massively parallel manner, all the possible computations of a nondeterministic Turing machine; characterizations of several complexity classes, like NP, P and PSPACE, were obtained in this framework. However, these machines were, generally, used to accept languages, not to decide them; in the case when a deciding model was considered ( [5]), the rejecting condition was just a mimic of the rejecting condition from classical computing models.…”
Section: Introductionmentioning
confidence: 99%