6,180 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
(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
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A parallel genetic algorithm for the Steiner Problem in Networks
This paper presents a parallel genetic algorithm to the
Steiner Problem in Networks. Several previous papers
have proposed the adoption of GAs and others
metaheuristics to solve the SPN demonstrating the
validity of their approaches. This work differs from them
for two main reasons: the dimension and the
characteristics of the networks adopted in the experiments
and the aim from which it has been originated. The reason
that aimed this work was namely to build a comparison
term for validating deterministic and computationally
inexpensive algorithms which can be used in practical
engineering applications, such as the multicast
transmission in the Internet. On the other hand, the large
dimensions of our sample networks require the adoption
of a parallel implementation of the Steiner GA, which is
able to deal with such large problem instances
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
Genet: A Quickly Scalable Fat-Tree Overlay for Personal Volunteer Computing using WebRTC
WebRTC enables browsers to exchange data directly but the number of possible
concurrent connections to a single source is limited. We overcome the
limitation by organizing participants in a fat-tree overlay: when the maximum
number of connections of a tree node is reached, the new participants connect
to the node's children. Our design quickly scales when a large number of
participants join in a short amount of time, by relying on a novel scheme that
only requires local information to route connection messages: the destination
is derived from the hash value of the combined identifiers of the message's
source and of the node that is holding the message. The scheme provides
deterministic routing of a sequence of connection messages from a single source
and probabilistic balancing of newer connections among the leaves. We show that
this design puts at least 83% of nodes at the same depth as a deterministic
algorithm, can connect a thousand browser windows in 21-55 seconds in a local
network, and can be deployed for volunteer computing to tap into 320 cores in
less than 30 seconds on a local network to increase the total throughput on the
Collatz application by two orders of magnitude compared to a single core
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
Multi-Objective Big Data Optimization with jMetal and Spark
Big Data Optimization is the term used to refer to optimization problems which have to manage very large amounts of data. In this paper, we focus on the parallelization of metaheuristics with the Apache Spark cluster computing system for solving multi-objective Big Data Optimization problems. Our purpose is to study the influence of accessing data stored in the Hadoop File System (HDFS) in each evaluation step of a metaheuristic and to provide a software tool to solve these kinds of problems. This tool combines the jMetal multi-objective optimization framework with Apache Spark. We have carried out experiments to measure the performance of the proposed parallel infrastructure in an environment based on virtual machines in a local cluster comprising up to 100 cores. We obtained interesting results for computational e ort and propose guidelines to face multi-objective Big Data Optimization
problems.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech
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
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