Revistes Publicacions URV: Triangle - llenguatge, literatura, computació> 2012

Developing Tools for Networks of Processors

  • Identification data

    Identifier: RP:4416
    Handle: https://hdl.handle.net/20.500.11797/RP4416
    Authors:
    Rojas Siles, José MiguelCuéllar, MiguelAnguiano Rey, EloyDe Lara, JuanJiménez Martínez, AntonioNavarrete Navarrete, CarmenDel Rosal, EmilioDe la Cruz Echeandía, MarinaOrtega de la Puente, Alfonso
    Abstract:
    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.

  • Others:

    Author, as appears in the article.: Rojas Siles, José Miguel Cuéllar, Miguel Anguiano Rey, Eloy De Lara, Juan Jiménez Martínez, Antonio Navarrete Navarrete, Carmen Del Rosal, Emilio De la Cruz Echeandía, Marina Ortega de la Puente, Alfonso
    Keywords: language
    Abstract: 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.
    Journal publication year: 2012
    Publication Type: info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article