Network of Evolutionary Processors with Splicing Rules

Author(s):  
Ashish Choudhary ◽  
Kamala Krithivasan
Biosystems ◽  
2007 ◽  
Vol 87 (2-3) ◽  
pp. 111-116 ◽  
Author(s):  
Ashish Choudhary ◽  
Kamala Krithivasan

2008 ◽  
Vol 19 (05) ◽  
pp. 1113-1132 ◽  
Author(s):  
CEZARA DRĂGOI ◽  
FLORIN MANEA

In this paper we consider, from the descriptional complexity point of view, a model of computation introduced in [1], namely accepting network of evolutionary processors with filtered connections (ANEPFCs). First we show that for each morphism h : V → W*, with V ∩ W = ∅, one can effectively construct an ANEPFC, of size 6 + |W|, which accepts every input word w and, at the end of the computation on this word, obtains h(w) in its output node. This result can be applied in constructing two different ANEPFCs, with 27 and, respectively, 26 processors, recognizing a given recursively enumerable language. The first architecture, based on the construction of a universal ANEPFC, has the property that only 7 of its 27 processors depend on the accepted language. On the other hand, all the 26 processors of the second architecture depend on the accepted language, but, differently from the first one, this network simulates efficiently (from both time and space perspectives) a nondeterministic Turing machine accepting the given language.


2006 ◽  
Vol 135 (3) ◽  
pp. 95-105 ◽  
Author(s):  
Florin Manea ◽  
Carlos Martín-Vide ◽  
Victor Mitrana

Axioms ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 183
Author(s):  
José Ángel Sánchez Martín ◽  
Victor Mitrana

In this paper, we propose direct simulations between a given network of evolutionary processors with an arbitrary topology of the underlying graph and a network of evolutionary processors with underlying graphs—that is, a complete graph, a star graph and a grid graph, respectively. All of these simulations are time complexity preserving—namely, each computational step in the given network is simulated by a constant number of computational steps in the constructed network. These results might be used to efficiently convert a solution of a problem based on networks of evolutionary processors provided that the underlying graph of the solution is not desired.


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