On the size complexity of hybrid networks of evolutionary processors

Castellanos, Juan; Leupold, Peter; Mitrana, Víctor

doi:10.1016/j.tcs.2004.07.035

Cited by 22 publications

(11 citation statements)

References 14 publications

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“…It is known from [6] that there exists an HNEP with two nodes that generates a non-context-free context-sensitive language. We first show that to obtain computational completeness, GHNEPs need at least three nodes.…”

Section: Hneps Of Very Small Sizementioning

confidence: 99%

On the size of computationally complete hybrid networks of evolutionary processors

Alhazov

Csuhaj-Varjú

Martı́n-Vide

et al. 2009

Theoretical Computer Science

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a b s t r a c tA hybrid network of evolutionary processors (an HNEP) is a graph where each node is associated with an evolutionary processor (a special rewriting system), a set of words, an input filter and an output filter. Every evolutionary processor is given with a finite set of one type of point mutations (an insertion, a deletion or a substitution of a symbol) which can be applied to certain positions of a string over the domain of the set of these rewriting rules. The HNEP functions by rewriting the words that can be found at the nodes and then re-distributing the resulting strings according to a communication protocol based on a filtering mechanism. The filters are defined by certain variants of random-context conditions. HNEPs can be considered as both language generating devices (GHNEPs) and language accepting devices (AHNEPs). In this paper, by improving the previous results, we prove that any recursively enumerable language can be determined by a GHNEP and an AHNEP with 7 nodes. We also show that the families of GHNEPs and AHNEPs with 2 nodes are not computationally complete.

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Section: Hneps Of Very Small Sizementioning

confidence: 99%

On the size of computationally complete hybrid networks of evolutionary processors

Alhazov

Csuhaj-Varjú

Martı́n-Vide

et al. 2009

Theoretical Computer Science

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“…NEP as a generating device was first introduced in (Csuhaj- Varju and Salomaa, 1997;Csuhaj-Varjú and Mitrana, 2000). The topic is further investigated in (Castellanos et al, 2001), while further different variants of the generating machine are introduced and analyzed in (Castellanos et al, 2005;Manea, 2004;Manea and Mitrana, 2007;Margenstern et al, 2005;Martin-Vide et al, 2003).…”

Section: Introduction To Analysis Of Natural Languages With Neps and mentioning

confidence: 99%

PNEPS FOR SHALLOW PARSING - NEPs Extended For Parsing Applied To Shallow Parsing

García¹,

Puente²,

Pérez‐Marín³

2010

Proceedings of the 2nd International Conference on Agents and Artificial Intelligence

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“…The results in the size complexity area were focused mainly on finding the number of processors needed by Generating Hybrid Networks of Evolutionary Processors (GHNEPs) to generate either recursively enumerable languages or regular languages (see [2,6,7]). In the case of AHNEPs, the only result known regards the size of a network accepting recursively enumerable languages (see [5]).…”

Section: Introductionmentioning

confidence: 99%

“…rings, stars, grids etc. Networks of evolutionary processors with underlying graphs having these special forms have been considered in a series of papers [1,4,6,7]. We focus here on complete AHNEPs, i.e.…”

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confidence: 99%

On the recognition of context-free languages using accepting hybrid networks of evolutionary processors

Manea

2007

International Journal of Computer Mathematics

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In this paper we approach the problem of constructing an accepting device for the class of context-free languages, based on a newly defined computational model: Accepting Hybrid Network of Evolutionary Processors (AHNEPs). Although it is known that AHNEPs are Turing complete, and, consequently, for every context-free language, seen as a recursively enumerable language, we can construct an AHNEP that simulates the Turing machine accepting it, we choose a direct approach: the AHNEPs we design simulate the computation done by a non-deterministic push-down automata accepting the language. This approach leads to a more economic AHNEP since the number of processors we use depends linearly on the number of states and the number of working symbols of the automaton, while for the network obtained in the general case of recursively enumerable languages, the number of processors is linear in the number of states, but quadratic in the number of working symbols of a Turing machine accepting the given language. Finally, we particularize the AHNEP architecture we proposed in order to recognize regular languages. We obtain an upper bound for the number of processors needed close to that known for generating hybrid networks of evolutionary processors.

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On the size complexity of hybrid networks of evolutionary processors

Cited by 22 publications

References 14 publications

On the size of computationally complete hybrid networks of evolutionary processors

On the size of computationally complete hybrid networks of evolutionary processors

PNEPS FOR SHALLOW PARSING - NEPs Extended For Parsing Applied To Shallow Parsing

On the recognition of context-free languages using accepting hybrid networks of evolutionary processors

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