Hybrid Networks Of Evolutionary Processors Research Articles

Computational completeness of complete, star-like, and linear hybrid networks of evolutionary processors with a small number of processors

A hybrid network of evolutionary processors (HNEP) is a graph where each node is associated with a special rewriting system called an evolutionary processor, an input filter, and an output filter. Each evolutionary processor is given a finite set of one type of point mutations (insertion, deletion or a substitution of a symbol) which can be applied to certain positions in a string. An HNEP rewrites the strings in the nodes and then re-distributes them according to a filter-based communication protocol; the filters are defined by certain variants of random-context conditions. HNEPs can be considered both as languages generating devices (GHNEPs) and language accepting devices (AHNEPs); most previous approaches treated the accepting and generating cases separately. For both cases, in this paper we show that five nodes are sufficient to accept (AHNEPs) or generate (GHNEPs) any recursively enumerable language by showing the more general result that any partial recursive relation can be computed by an HNEP with (at most) five nodes with the underlying graph structure for the communication between the evolutionary processors being the complete or the linear graph with five nodes, whereas with a star-like communication graph we need six nodes. If the final results are defined by only taking the terminal strings out of the designated output node, then for these extended HNEPs we can prove that only four nodes are needed in all cases--for computing any partial recursive relation as well as for generating and accepting any recursively enumerable language--and the underlying communication structure can be a complete or a linear graph, but now even a star-like graph, too.

FILTER POSITION IN NETWORKS OF SUBSTITUTION PROCESSORS DOES NOT MATTER

It is known ([4]) that moving the filters from the nodes to the edges in accepting hybrid networks of evolutionary processors does not decrease the computational power of the model which equals that of a Turing machine. A direct and time complexity preserving simulation is presented in [1]. All three types of processors (substitution, insertion, deletion) are essentially used in this simulation. In this note we prove that such a direct simulation between networks containing substitution nodes only still exists.

The role of evolutionary operations in accepting hybrid networks of evolutionary processors

In this paper, we investigate the role of evolutionary operations in accepting hybrid networks of evolutionary processors (AHNEP for short) in the following way. We consider AHNEPs with all the nodes specialized in only one evolutionary operation (substitution, insertion, or deletion) or in two operations out of these three. The considered variants differ in two respects: filters that are used to control the exchange of information (we use random context conditions and regular languages as filters) and the way of accepting the input word (at least one output node or all output nodes are non-empty at some moment in the computation). The computational power of all these variants is studied.

Accepting Hybrid Networks of Evolutionary Processors with Special Topologies and Small Communication

Starting from the fact that complete Accepting Hybrid Networks of Evolutionary Processors allow much communication between the nodes and are far from network structures used in practice, we propose in this paper three network topologies that restrict the communication: star networks, ring networks, and grid networks. We show that ring-AHNEPs can simulate 2-tag systems, thus we deduce the existence of a universal ring-AHNEP. For star networks or grid networks, we show a more general result; that is, each recursively enumerable language can be accepted efficiently by a star- or grid-AHNEP. We also present bounds for the size of these star and grid networks. As a consequence we get that each recursively enumerable can be accepted by networks with at most 13 communication channels and by networks where each node communicates with at most three other nodes.

Small universal accepting hybrid networks of evolutionary processors

In this paper, we improve some results regarding the size complexity of accepting hybrid networks of evolutionary processors (AHNEPs). We show that there are universal AHNEPs of size 6, by devising a method for simulating 2-tag systems. This result improves the best upper bound for the size of universal AHNEPs which was 7. We also propose a computationally and descriptionally efficient simulation of nondeterministic Turing machines with AHNEPs. More precisely, we prove that AHNEPs with ten nodes can simulate any nondeterministic Turing machine of time complexity f (n) in time O(f (n)). This result significantly improves the best known upper bound for the number of nodes in a network simulating in linear time an arbitrary Turing machine, namely 24.

On the size of computationally complete hybrid networks of evolutionary processors

A 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.

Breaking the data encryption standard using networks of evolutionary processors with parallel string rewriting rules

In this paper we introduce a biologically inspired distributed computing model called networks of evolutionary processors with parallel string rewriting rules (NEPPS), which is a variation of the hybrid networks of evolutionary processors introduced by Martin-Vide et al. Such a network contains simple processors that are located in the nodes of a virtual graph. Each processor has strings (each string having multiple copies) and string rewriting rules. The rules are applied parallely on the strings. After the strings have been rewritten, they are communicated among the processors through filters. We show that we can theoretically break the DES (data encryption standard), which is the most widely used cryptosystem, using NEPPS. We prove that, given an arbitrary <plain-text, cipher-text> pair, one can recover the DES key in a constant number of steps.

A New Characterization of NP, P, and PSPACE with Accepting Hybrid Networks of Evolutionary Processors

We consider three complexity classes defined on Accepting Hybrid Networks of Evolutionary Processors (AHNEP) and compare them with the classical complexity classes defined on the standard computing model of Turing machine. By definition, AHNEPs are deterministic. We prove that the classical complexity class NP equals the family of languages decided by AHNEPs in polynomial time. A language is in P if and only if it is decided by an AHNEP in polynomial time and space. We also show that PSPACE equals the family of languages decided by AHNEPs in polynomial length.

On the size complexity of universal accepting hybrid networks of evolutionary processors

In this paper we discuss the following interesting question about accepting hybrid networks of evolutionary processors (AHNEP), which are a recently introduced bio-inspired computing model. The question is: how many processors are required in such a network to recognise a given language L? Two answers are proposed for the most general case, when L is a recursively enumerable language, and both answers improve on the previously known bounds. In the first case the network has a number of processors that is linearly bounded by the cardinality of the tape alphabet of a Turing machine recognising the given language L. In the second case we show that an AHNEP with a fixed underlying structure can accept any recursively enumerable language. The second construction has another useful property from a practical point of view as it includes a universal AHNEP as a subnetwork, and hence only a limited number of its parameters depend on the given language.

All NP-problems can be solved in polynomial time by accepting hybrid networks of evolutionary processors of constant size

In this paper, we present a new result regarding Accepting Hybrid Networks of Evolutionary Processors (AHNEP for short): we propose a method for constructing, for every NP-language, an AHNEP of size 24 deciding that language in polynomial time. While the number of nodes of this AHNEP does not depend on the language, the other parameters of the network (rules, symbols, filters) depend on it. Since each AHNEP may be viewed as a problem solver as shown in [C. Martín-Vide, V. Mitrana, Networks of evolutionary processors: results and perspectives, in: Molecular Computational Models: Unconventional Approaches, Idea Group Publishing, Hershey, 2005, pp. 78–114], the later result may be interpreted as a method for solving every NP-problem in polynomial time by AHNEPs of constant size.

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

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.

Hybrid networks of evolutionary processors are computationally complete

A hybrid network of evolutionary processors (an HNEP) consists of several language processors which are located in the nodes of a virtual graph and able to perform only one type of point mutations (insertion, deletion, substitution) on the words found in that node, according to some predefined rules. Each node is associated with an input and an output filter, defined by some random-context conditions. After applying in parallel a point mutation to all the words existing in every node, the new words which are able to pass the output filter of the respective node navigate simultaneously through the network and enter those nodes whose input filter they are able to pass. We show that even the so-called elementary HNEPs are computationally complete. In this case every node is able to perform only one instance of the specified operation: either an insertion, or a deletion, or a substitution of a certain symbol. We also prove that in the case of non-elementary networks, any recursively enumerable language over a common alphabet can be obtained with an HNEP whose underlying structure is a fixed graph depending on the common alphabet only.

On the size complexity of hybrid networks of evolutionary processors

The goal of this paper is twofold. Firstly, to survey in a systematic and uniform way the main results regarding the size descriptional complexity measures of hybrid networks of evolutionary processors as generating devices. Secondly, we improve some results about a size measure, prove that it is connected, and discuss the possibility of computing this measure for regular and context-free languages. We also briefly present a few NP-complete problems and recall how they can be solved in linear time by accepting networks of evolutionary processors with linearly bounded resources (nodes, rules, symbols). Finally, the size complexity of accepting hybrid networks of evolutionary processors recognizing all NP languages in polynomial time is briefly discussed.