17 research outputs found
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
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
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
Hierarchical Logical Description and Neural Recognition of Complex Patterns
Authors suggested earlier hierarchical method for definition of class description at pattern recognition
problems solution. In this paper development and use of such hierarchical descriptions for parallel representation
of complex patterns on the base of multi-core computers or neural networks is proposed
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
Generating networks of genetic processors
[EN] The Networks of Genetic Processors (NGPs) are non-conventional models of computation based on genetic operations over strings, namely mutation and crossover operations as it was established in genetic algorithms. Initially, they have been proposed as acceptor machines which are decision problem solvers. In that case, it has been shown that they are universal computing models equivalent to Turing machines. In this work, we propose NGPs as enumeration devices and we analyze their computational power. First, we define the model and we propose its definition as parallel genetic algorithms. Once the correspondence between the two formalisms has been established, we carry out a study of the generation capacity of the NGPs under the research framework of the theory of formal languages. We investigate the relationships between the number of processors of the model and its generative power. Our results show that the number of processors is important to increase the generative capability of the model up to an upper bound, and that NGPs are universal models of computation if they are formulated as generation devices. This allows us to affirm that parallel genetic algorithms working under certain restrictions can be considered equivalent to Turing machines and, therefore, they are universal models of computation.This research was partially supported by TAILOR, a project funded by EU Horizon 2020 research and innovation programme under GA No 952215.Campos Frances, M.; Sempere Luna, JM. (2022). Generating networks of genetic processors. Genetic Programming and Evolvable Machines. 23(1):133-155. https://doi.org/10.1007/s10710-021-09423-713315523
Networks of Evolutionary Processors: Java Implementation of a Threaded Processor
This paper is focused on a parallel JAVA implementation of a processor defined in a Network of
Evolutionary Processors. Processor description is based on JDom, which provides a complete, Java-based
solution for accessing, manipulating, and outputting XML data from Java code. Communication among different
processor to obtain a fully functional simulation of a Network of Evolutionary Processors will be treated in future.
A safe-thread model of processors performs all parallel operations such as rules and filters. A non-deterministic
behavior of processors is achieved with a thread for each rule and for each filter (input and output). Different
results of a processor evolution are shown
Simulating NEPs in a cluster with jNEP
This paper introduces jNEP: a general, flexible, and rigorous implementation
of NEPs (the basic model) and some interestenting variants; it is specifically designed to
easily add the new results (filters, stopping conditions, evolutionary rules, and so on) of the
research in the area. jNEP is written in Java; there are two different versions that implement
the concurrency of NEPs by means of the Java classes Process and Threads respectively.
There are also extended versions that run on clusters of computers under JavaParty. jNEP
reads the description of the currently simulated NEP from a XML configuration file. This
paper shows how jNEP tackles the SAT problem with polynomial performance by simulating
an ANSP.This work was supported in part by the Spanish Ministry of Education and Science (MEC) under Project TSI2005-08225-C07-06
An Architecture forRepresenting Biological Processes based on Networks of Bio-inspired Processors
n this paper we propose the use of Networks of Bio-inspired Processors (NBP) to model some biological phenomena within a computational framework. In particular, we propose the use of an extension of NBP named Network Evolutionary Processors Transducers to simulate chemical transformations of substances. Within a biological process, chemical transformations of substances are basic operations in the change of the state of the cell. Previously, it has been proved that NBP are computationally complete, that is, they are able to solve NP complete problems in linear time, using massively parallel computations. In addition, we propose a multilayer architecture that will allow us to design models of biological processes related to cellular communication as well as their implications in the metabolic pathways. Subsequently, these models can be applied not only to biological-cellular instances but, possibly, also to configure instances of interactive processes in many other fields like population interactions, ecological trophic networks, in dustrial ecosystems, etc