4,970 research outputs found
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
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
Extended Networks of Evolutionary Processors
This paper presents an extended behavior of networks of evolutionary processors. Usually, such nets
are able to solve NP-complete problems working with symbolic information. Information can evolve applying rules
and can be communicated though the net provided some constraints are verified. These nets are based on
biological behavior of membrane systems, but transformed into a suitable computational model. Only symbolic
information is communicated. This paper proposes to communicate evolution rules as well as symbolic
information. This idea arises from the DNA structure in living cells, such DNA codes information and operations
and it can be sent to other cells. Extended nets could be considered as a superset of networks of evolutionary
processors since permitting and forbidden constraints can be written in order to deny rules communication
Recent Advances in Graph Partitioning
We survey recent trends in practical algorithms for balanced graph
partitioning together with applications and future research directions
Parallel Graph Partitioning for Complex Networks
Processing large complex networks like social networks or web graphs has
recently attracted considerable interest. In order to do this in parallel, we
need to partition them into pieces of about equal size. Unfortunately, previous
parallel graph partitioners originally developed for more regular mesh-like
networks do not work well for these networks. This paper addresses this problem
by parallelizing and adapting the label propagation technique originally
developed for graph clustering. By introducing size constraints, label
propagation becomes applicable for both the coarsening and the refinement phase
of multilevel graph partitioning. We obtain very high quality by applying a
highly parallel evolutionary algorithm to the coarsened graph. The resulting
system is both more scalable and achieves higher quality than state-of-the-art
systems like ParMetis or PT-Scotch. For large complex networks the performance
differences are very big. For example, our algorithm can partition a web graph
with 3.3 billion edges in less than sixteen seconds using 512 cores of a high
performance cluster while producing a high quality partition -- none of the
competing systems can handle this graph on our system.Comment: Review article. Parallelization of our previous approach
arXiv:1402.328
Networks of Evolutionary Processors (NEP) as Decision Support Systems
This paper presents the application of Networks of Evolutionary Processors to Decision Support
Systems, precisely Knowledge-Driven DSS. Symbolic information and rule-based behavior in Networks of
Evolutionary Processors turn out to be a great tool to obtain decisions based on objects present in the network.
The non-deterministic and massive parallel way of operation results in NP-problem solving in linear time.
A working NEP example is shown
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
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
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