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

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

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

Desarrollode un simulador de redes de procesadores que evolucionan (NEPS) en la nube (SPARK)

Máster Universitario en Investigación e Innovación en Tecnologías de la Información y las Comunicaciones (i2-TIC)The natural-inspired computing has becomeone of the most frequently used techniques to handle complex problems such as the NP-Hard optimization problems. This kind of computing has several advantages over traditional computing, including resiliency, parallel data processing, and low consumptionof power. One of the active research areas of the natural-inspired algorithms is Network of Evolutionary Processors (NEPs). A NEP consists of several cells that are attached together; at the same time the edges of the graph are to transfer data between the nodes in system, while cells are representing the nodes.In this thesis we construct a NEPs system which is implemented over the Hadoop spark environment. The use of the spark platform is essential in this work due to the capabilities supplied by this platform. It is a suitable environment used solving some complicated problems. Using the environment is a possible choice in order to design the NEPs system. For this reason, in this thesis, we detailed on how to install, design and operate this system on the Apache the spark environment is used because it has the capability to implement the NEPs system in a distributed manner. The NEPs simulation is delivered in this work. An analysis of system’s parameters was also provided in this work for the system performance evaluation via the examination of each single factor affecting the performance of the NEPs individually. After testing the system, it become clear that using NEPs on the decentralize cloud eco-system can be thought as an effective method to handle data of different formats and also to execute optimization problems such as Adelman, 3-colorabilty and Massive-NEP problems. Moreover, this scheme is also robust that can be adaptable to handle data which might be scaled up to be big data which is characterized by its volume and heterogeneity. In this context heterogeneity might be referring to collecting data from different sources. Moreover, the utilization of the spark environment as a platform to operate the NEPs system has it is prospects. This environment is characterized by its fast task handing chunks of data to Hadoop architecture that is used to implement the spark system which is mainly based on the map and reduce functions. Thus, the task is distributed on NEPs system using the cloud based environment system made it possible to have logical result in all of the three examples investigated and examined in this method

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

Simulating accepting networks of evolutionary processors with filtered connections by accepting evolutionary P systems

In this work, we propose a variant of P system based on the rewriting of string-objects by means of evolutionary rules. The membrane structure of such a P system seems to be a very natural tool for simulating the filters in accepting networks of evolutionary processors with filtered connections. We discuss an informal construction supporting this simulation. A detailed proof is to be considered in an extended version of this work

Desarrollo de entorno online de programación para computación natural

Máster en Investigación e Innovación en Tecnologías de la Información y las Comunicaciones (i2- TIC).This work proposes a natural computer programming (for CA and NEPs) environment platform using Blockly. The platform is a web-based tool that provides simulators for two well-known natural computing systems: Cellular Automata (CA) and Network of Evolutionary Processors (NEPs). CA programming blocks presented in this work provide the ability to design and implement several types of CA including Elementary cellular automata, 2D cellular automata, and nD cellular automata. The tool also provides a graphical representation of CA’s grid through projection for any CA that has 3 or more dimensions. A NEPs Blockly programming environment is presented in this work. It provides the ability to design and simulate NEPs. Blocks are used as flexible user interface to enter NEPs specifications. The blocks automatically generate a standard XML configurations code which can be sent to the server side of the simulator for implementation. The tool also provides a graphical representation for the static topology of the system. Both CA and NEPs Blockly programming environments have been tested in several rather academic examples. The work presents an online simulation platform for natural computing algorithm using visual programing tool, namely Blockly. The proposed platform provides software engineering tools for setting up algorithms and also ease of use especially for teaching of these algorithm. The software engineering tools has been implemented on the NEPs as there is much more software tools already presented for cellular automata. The software designed for NEPs are a set of blocks to implement several types of connections between nodes. These blocks reduce time and complexity in setting up NEPs with fully connected nodes, for instance. In the other hand, cellular automata algorithm has been chosen to test the ease of the process of teaching and learning natural computing algorithms as they are much better-known model. The test has been conducted with students, teachers and researchers. Results of the experiment showed that the CA Blockly simulator outperforms traditional manual methods of implementing CA. It also showed that the proposed environment has desired features such as ease of use and decreases learning time. The NEPs part of the system has been tested against several applications. It showed that it provides a flexible designing tool for NEPs. It outperforms traditional XML coding methods in terms of ease of use and designing time. In addition we have designed specific high level constructs that automatize in some way the specific of complex NEPs’ topologies by hand. They could be considered as embryonic software engineering tools to program NEPs. Our tool is considered a generic platform for web-based implementation. It has desired features and wide range of properties that could attract the scientific community to adapt and develop in the future

Developing Tools for Networks of Processors

A great deal of research eort is currently being made in the realm of so called natural computing. Natural computing mainly focuses on the denition, formal description, analysis, simulation and programming of new models of computation (usually with the same expressive power as Turing Machines) inspired by Nature, which makes them particularly suitable for the simulation of complex systems.Some of the best known natural computers are Lindenmayer systems (Lsystems, a kind of grammar with parallel derivation), cellular automata, DNA computing, genetic and evolutionary algorithms, multi agent systems, arti- cial neural networks, P-systems (computation inspired by membranes) and NEPs (or networks of evolutionary processors). This chapter is devoted to this last model