Evolution-Communication P Systems: Time-Freeness

Membrane computing is a (biologically motivated) theoretical framework of distributed parallel computing. If symbol-objects are considered, then membrane sys- tems (also called P systems) are distributed multiset processing systems. In evolution- communication (EC) P systems the computation is carried out with the use of non- cooperative rewriting rules and with (usually the minimally cooperative) transport rules. The goal of this article is to improve the existing results on evolution-communication P systems. It is known that EC P systems with 2 membranes are universal, and so are time-free EC P systems with targets with 3 membranes. We prove that any recursively enumerable set of vectors of nonnegative integers can be generated by time-free EC P systems (without targets) with 2 membranes, thus improving both results

An unsupervised learning algorithm for membrane computing

This paper focuses on the unsupervised learning problem within membrane computing, and proposes an innovative solution inspired by membrane computing techniques, the fuzzy membrane clustering algorithm. An evolution–communication P system with nested membrane structure is the core component of the algorithm. The feasible cluster centers are represented by means of objects, and three types of membranes are considered: evolution, local store, and global store. Based on the designed membrane structure and the inherent communication mechanism, a modified differential evolution mechanism is developed to evolve the objects in the system. Under the control of the evolution–communication mechanism of the P system, the proposed fuzzy clustering algorithm achieves good fuzzy partitioning for a data set. The proposed fuzzy clustering algorithm is compared to three recently-developed and two classical clustering algorithms for five artificial and five real-life data sets.National Natural Science Foundation of China No 61170030National Natural Science Foundation of China No 61472328Chunhui Project Foundation of the Education Department of China No. Z2012025Chunhui Project Foundation of the Education Department of China No. Z2012031Sichuan Key Technology Research and Development Program No. 2013GZX015

On Distributed Solution to SAT by Membrane Computing

Tissue P systems with evolutional communication rules and cell division (TPec, for short) are a class of bio-inspired parallel computational models, which can solve NP-complete problems in a feasible time. In this work, a variant of TPec, called $k$-distributed tissue P systems with evolutional communication and cell division ($k\text{-}\Delta_{TP_{ec}}$, for short) is proposed. A uniform solution to the SAT problem by $k\text{-}\Delta_{TP_{ec}}$ under balanced fixed-partition is presented. The solution provides not only the precise satisfying truth assignments for all Boolean formulas, but also a precise amount of possible such satisfying truth assignments. It is shown that the communication resource for one-way and two-way uniform $k$-P protocols are increased with respect to $k$; while a single communication is shown to be possible for bi-directional uniform $k$-P protocols for any $k$. We further show that if the number of clauses is at least equal to the square of the number of variables of the given boolean formula, then $k\text{-}\Delta_{TP_{ec}}$ for solving the SAT problem are more efficient than TPec as show in \cite{bosheng2017}; if the number of clauses is equal to the number of variables, then $k\text{-}\Delta_{TP_{ec}}$ for solving the SAT problem work no much faster than TPec

On Determinism of Evolution-Communication P Systems

It is commonly believed that a significant part of the computational power of membrane systems comes from their inherent non-determinism. Recently, R. Freund and Gh. Paun have considered deterministic P systems, and formulated the general question whether the computing (generative) capacity of non-deterministic P systems is strictly larger than the (accepting) capacity of their deterministic counterpart. In this paper, we study the computational power of deterministic P systems in the evolution{communication framework. It is known that, in the generative case, two membranes are enough for universality. For the deterministic systems, we obtain the universality with three membranes, leaving the original problem open