13,516 research outputs found
Hybrid Networks of Evolutionary Processors
A hybrid network of evolutionary processors consists of several
processors which are placed in nodes of a virtual graph and can
perform one simple operation only on the words existing in that node
in accordance with some strategies. Then the words which can pass the
output filter of each node navigate simultaneously through the network
and enter those nodes whose input filter was passed. We prove that these
networks with filters defined by simple random-context conditions, used
as language generating devices, are able to generate all linear languages
in a very efficient way, as well as non-context-free languages. Then, when
using them as computing devices, we present two linear solutions of the
Common Algorithmic Problem.Ministerio de Ciencia y Tecnología TIC2002-04220-C03-0
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
(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
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
What is Computational Intelligence and where is it going?
What is Computational Intelligence (CI) and what are its relations with Artificial Intelligence (AI)? A brief survey of the scope of CI journals and books with ``computational intelligence'' in their title shows that at present it is an umbrella for three core technologies (neural, fuzzy and evolutionary), their applications, and selected fashionable pattern recognition methods. At present CI has no comprehensive foundations and is more a bag of tricks than a solid branch of science. The change of focus from methods to challenging problems is advocated, with CI defined as a part of computer and engineering sciences devoted to solution of non-algoritmizable problems. In this view AI is a part of CI focused on problems related to higher cognitive functions, while the rest of the CI community works on problems related to perception and control, or lower cognitive functions. Grand challenges on both sides of this spectrum are addressed
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
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