4,440 research outputs found
Accepting Hybrid Networks of Evolutionary Processors
We consider time complexity classes defined on accepting hybrid
networks of evolutionary processors (AHNEP) similarly to the classical
time complexity classes defined on the standard computing model
of Turing machine. By definition, AHNEPs are deterministic. We prove
that the classical complexity class NP equals the set of languages accepted
by AHNEPs in polynomial time
Accepting Hybrid Networks of Evolutionary Processors with Special Topologies and Small Communication
Starting from the fact that complete Accepting Hybrid Networks of
Evolutionary Processors allow much communication between the nodes and are far
from network structures used in practice, we propose in this paper three
network topologies that restrict the communication: star networks, ring
networks, and grid networks. We show that ring-AHNEPs can simulate 2-tag
systems, thus we deduce the existence of a universal ring-AHNEP. For star
networks or grid networks, we show a more general result; that is, each
recursively enumerable language can be accepted efficiently by a star- or
grid-AHNEP. We also present bounds for the size of these star and grid
networks. As a consequence we get that each recursively enumerable can be
accepted by networks with at most 13 communication channels and by networks
where each node communicates with at most three other nodes.Comment: In Proceedings DCFS 2010, arXiv:1008.127
A New Characterization of NP, P, and PSPACE with Accepting Hybrid Networks of Evolutionary Processors
We consider three complexity classes defined on Accepting Hybrid Networks
of Evolutionary Processors (AHNEP) and compare them with the classical
complexity classes defined on the standard computing model of Turing machine. By
definition, AHNEPs are deterministic. We prove that the classical complexity class
NP equals the family of languages decided by AHNEPs in polynomial time. A language
is in P if and only if it is decided by an AHNEP in polynomial time and space.
We also show that PSPACE equals the family of languages decided by AHNEPs in
polynomial length
Small Universal Accepting Networks of Evolutionary Processors with Filtered Connections
In this paper, we present some results regarding the size complexity of
Accepting Networks of Evolutionary Processors with Filtered Connections
(ANEPFCs). We show that there are universal ANEPFCs of size 10, by devising a
method for simulating 2-Tag Systems. This result significantly improves the
known upper bound for the size of universal ANEPFCs which is 18.
We also propose a new, computationally and descriptionally efficient
simulation of nondeterministic Turing machines by ANEPFCs. More precisely, we
describe (informally, due to space limitations) how ANEPFCs with 16 nodes can
simulate in O(f(n)) time any nondeterministic Turing machine of time complexity
f(n). Thus the known upper bound for the number of nodes in a network
simulating an arbitrary Turing machine is decreased from 26 to 16
(Tissue) P Systems with Vesicles of Multisets
We consider tissue P systems working on vesicles of multisets with the very
simple operations of insertion, deletion, and substitution of single objects.
With the whole multiset being enclosed in a vesicle, sending it to a target
cell can be indicated in those simple rules working on the multiset. As
derivation modes we consider the sequential mode, where exactly one rule is
applied in a derivation step, and the set maximal mode, where in each
derivation step a non-extendable set of rules is applied. With the set maximal
mode, computational completeness can already be obtained with tissue P systems
having a tree structure, whereas tissue P systems even with an arbitrary
communication structure are not computationally complete when working in the
sequential mode. Adding polarizations (-1, 0, 1 are sufficient) allows for
obtaining computational completeness even for tissue P systems working in the
sequential mode.Comment: In Proceedings AFL 2017, arXiv:1708.0622
(Tissue) P Systems with Vesicles of Multisets
We consider tissue P systems working on vesicles of multisets with the very
simple operations of insertion, deletion, and substitution of single objects.
With the whole multiset being enclosed in a vesicle, sending it to a target
cell can be indicated in those simple rules working on the multiset. As
derivation modes we consider the sequential mode, where exactly one rule is
applied in a derivation step, and the set maximal mode, where in each
derivation step a non-extendable set of rules is applied. With the set maximal
mode, computational completeness can already be obtained with tissue P systems
having a tree structure, whereas tissue P systems even with an arbitrary
communication structure are not computationally complete when working in the
sequential mode. Adding polarizations (-1, 0, 1 are sufficient) allows for
obtaining computational completeness even for tissue P systems working in the
sequential mode.Comment: In Proceedings AFL 2017, arXiv:1708.0622
Networks of Bio-inspired Processors
The goal of this work is twofold. Firstly, we propose a uniform view of three types of accepting networks of bio-inspired processors: networks of evolutionary processors, networks of splicing processors and networks of genetic processors. And, secondly, we survey some features of these networks: computational power, computational and descriptional complexity, the existence of universal networks, eciency as problem solvers and the relationships among them
Networks of polarized evolutionary processors are computationally complete
ABSTRACT
In this paper, we consider the computational power of a new variant of networks of evolutionary processors which seems to be more suitable for a software and hardware implementation. Each processor as well as the data navigating throughout the network are now considered to be polarized. While the polarization of every processor is predefined, the data polarization is dynamically computed by means of a valuation mapping. Consequently, the protocol of communication is naturally defined by means of this polarization. We show that tag systems can be simulated by these networks with a constant number of nodes, while Turing machines can be simulated, in a time-efficient way, by these networks with a number of nodes depending linearly on the tape alphabet of the Turing machine
Rule Representation in Distributed Environments with Accepting Networks of Splicing Processors.
This paper presents the model named Accepting Networks of
Evolutionary Processors as NP-problem solver inspired in the biological DNA operations. A processor has a rules set, splicing rules in this model,an object multiset and a filters set. Rules can be applied in parallel since there exists a large number of copies of objects in the multiset.
Processors can form a graph in order to solve a given problem. This paper shows the network configuration in order to solve the SAT problem using linear resources and time. A rule representation arquitecture in distributed environments can be easily implemented using these networks
of processors, such as decision support systems, as shown in the paper
Simulating accepting networks of evolutionary processors with filtered connections by accepting evolutionary P systems
In this work, we propose a variant of P system based on the rewriting of string-objects by means of evolutionary rules. The membrane structure of such a P system seems to be a very natural tool for simulating the filters in accepting networks of evolutionary processors with filtered connections. We discuss an informal construction supporting this simulation. A detailed proof is to be considered in an extended version of this work
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