21 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
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
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
P Systems with Minimal Left and Right Insertion and Deletion
In this article we investigate the operations of insertion and deletion performed
at the ends of a string. We show that using these operations in a P systems
framework (which corresponds to using specific variants of graph control), computational
completeness can even be achieved with the operations of left and right insertion and
deletion of only one symbol
Transducers based on networks of evolutionary processors LOS FINANCIADORES NO ESTÁN BIEN
We consider a new type of transducer that does not scan sequentially the input word. Instead, it consists of a directed graph whose nodes are processors which work in parallel and are specialized in just one type of a very simple evolutionary operation: inserting, deleting or substituting a symbol by another one. The computation on an input word starts with this word placed in a designated node, the input node, of the network an alternates evolutionary and communication steps. The computation halts as soon as another designated node, the output node, is nonempty. The translation of the input word is the set of words existing in the output node when the computation halts. We prove that these transducers can simulate the work of generalized sequential machines on every input. Furthermore, all words obtained by a given generalized sequential machine by the shortest computations on a given word can also be computed by the new transducers. Unlike the case of generalized sequential machines, every recursively enumerable language can be the transduction de?ned by the new transducer of a very simple regular language. The same idea may be used for proving that these transducers can simulate the shortest computations of an arbitrary Turing machine, used as a transducer, on every input word. Finally, we consider a restricted variant of NEP transducer, namely pure NEP transducers and prove that there are still regular languages whose pure NEP transductions are not semilinear
P Systems with Minimal Left and Right Insertion and Deletion
Summary. In this article we investigate the operations of insertion and deletion performed at the ends of a string. We show that using these operations in a P systems framework (which corresponds to using specific variants of graph control), computational completeness can even be achieved with the operations of left and right insertion and deletion of only one symbol.
String Measure Applied to String Self-Organizing Maps and Networks of Evolutionary Processors
* Supported by projects CCG08-UAM TIC-4425-2009 and TEC2007-68065-C03-02This paper shows some ideas about how to incorporate a string learning stage in self-organizing
algorithms. T. Kohonen and P. Somervuo have shown that self-organizing maps (SOM) are not restricted to
numerical data. This paper proposes a symbolic measure that is used to implement a string self-organizing map
based on SOM algorithm. Such measure between two strings is a new string. Computation over strings is
performed using a priority relationship among symbols; in this case, symbolic measure is able to generate new
symbols. A complementary operation is defined in order to apply such measure to DNA strands. Finally, an
algorithm is proposed in order to be able to implement a string self-organizing map