Networks of evolutionary processors

Castellanos, Juan; Martı́n-Vide, Carlos; Mitrana, Víctor; Sempere, José M.

doi:10.1007/s00236-003-0114-y

Cited by 102 publications

(74 citation statements)

References 5 publications

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“…The classic family of NEPs uses two kinds of filters (input and ouput) each of which is defined by means of a couple of components (forbidden and permitted strings) One of the main characteristics of NEPs is that they are intrinsically parallel and some instances of them have the same computational power of Turing's machine giving the possibility of designing algorithms for NP-problems that improve the temporal performance of their Turing counterparts. More detailed formal definitions and properties could be found in [1] One of the most interesting features of bio-inspired computers, like NEPs, is their intrinsic parallelism. We can design algorithms for them that could improve the exponential performance of their classic versions, but, unfortunately there are neither no real computers nor programming languages and software engineering tools for almost any bioinspired model.…”

Section: Motivationmentioning

confidence: 99%

Different Approaches for Development Tools for Natural Computers - Grammar Driven vs. Model Driven Approaches

Fernández¹,

Saiz²,

Echeandía³

et al. 2013

Proceedings of the 5th International Conference on Agents and Artificial Intelligence

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Abstract. In this paper we will compare our first steps in two different approaches to define programming languages for NEPs (one bio-inspired model of computation in which our research group is interested). The classic approach proposed by the literature several decades ago is focused on the grammar of the syntax of the language being defined. Recently the focus is moved to a formal description (model) of the model of computation for which the programming language is being designed. This approach is called model driven. The designer simply adds syntax, semantics checks and translation routines to the different elements of the model that are applied. Programming is usually understood as instantiating the model. After introducing the main characteristics of each model for this particular case some conclusions and further research tasks are discussed. MotivationOur research group is interested in providing the scientific community with powerful tools to develop software applications to solve problems by means of new computing devices (most of them inspired by the way in which Nature solves difficult tasks). One of these new computing paradigms are Networks of Evolutionary Processors (NEPs) that mainly consist of a set of processors each of which performs a very simple process on the set of strings that they contain. These computing processors are connected as nodes of a graph. NEPs are not allowed to dynamically change the topology of their nets. The complete system alternates computing and communicating steps. In the computing or evolving step the processors change their contents simultaneously. In the communicating step they share some of their strings with the rest of the processors to which they are connected in the net. An important component of NEPs are the filters used by the processors to decide which strings enter and leave the nodes. The classic family of NEPs uses two kinds of filters (input and ouput) each of which is defined by means of a

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Section: Motivationmentioning

confidence: 99%

Different Approaches for Development Tools for Natural Computers - Grammar Driven vs. Model Driven Approaches

Fernández¹,

Saiz²,

Echeandía³

et al. 2013

Proceedings of the 5th International Conference on Agents and Artificial Intelligence

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“…This work is a continuation of the investigation started in [1] and [2] where one has considered a mechanism inspired from cell biology, namely networks of evolutionary processors, that is networks whose nodes are very simple processors able to perform just one type of point mutation (insertion, deletion or substi-tution of a symbol). These nodes are endowed with filters which are defined by some membership or random context condition.…”

Section: Introductionmentioning

confidence: 99%

“…Mechanisms introduced in [1] and [2] simplify as much as possible the networks of parallel language processors defined in [4]. Thus, in each node is placed a very simple processor, called evolutionary processor, which is able to perform a simple rewriting operation only, namely either insertion of a symbol or substitution of a symbol by another, or deletion of a symbol.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, in each node is placed a very simple processor, called evolutionary processor, which is able to perform a simple rewriting operation only, namely either insertion of a symbol or substitution of a symbol by another, or deletion of a symbol. Furthermore, filters used in [4] are simplified in some versions defined in [1,2].…”

Section: Introductionmentioning

confidence: 99%

“…In [2] networks with at most six nodes having filters defined by the membership to a regular language condition are able to generate all recursively enumerable languages no matter the underlying structure. This result does not surprise since similar characterizations have been reported in the literature, see, e.g., [5,11,10,12,14].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Hybrid Networks of Evolutionary Processors

Martı́n-Vide

Mitrana

Pérez–Jiménez

et al. 2003

Genetic and Evolutionary Computation — GECCO 2003

Self Cite

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Abstract.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.

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