2010 **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.

Help me understand this report

Search citation statements

Paper Sections

Select...

1

1

1

1

Citation Types

0

14

0

2

Year Published

2011

2020

Publication Types

Select...

2

1

1

Relationship

0

4

Authors

Journals

(16 citation statements)

(35 reference statements)

0

14

0

2

“…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.…”

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.…”

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.…”

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.…”

confidence: 99%