AbstractWe propose a computational model that is inspired by genetic operations over strings such as mutation and crossover. The model, Accepting Network of Genetic Processors, is highly related to previously proposed ones such as Networks of Evolutionary Processors and Networks of Splicing Processors. These models are complete computational models inspired by DNA evolution and recombination. Here, we prove that the proposed model is computationally complete (it is equivalent to the Turing machine). Hence, it can accept any recursively enumerable language. In addition, we relate the proposed model with (parallel) Genetic Algorithms or Evolutionary Programs and we set these techniques as decision problem solvers
In this paper we simplify a recent model of computation considered in [Margenstern et al. 2005], nam...
* Supported by INTAS 00-626 and TIC 2003-09319-c03-03.This paper presents some connectionist models ...
We consider time complexity classes defined on accepting hybrid networks of evolutionary processors...
The goal of this work is twofold. Firstly, we propose a uniform view of three types of accepting net...
AbstractWe propose a construction of an accepting hybrid network of evolutionary processors (AHNEP) ...
AbstractThe Accepting Networks of Evolutionary Processors (ANEPs for short) are bio-inspired computa...
AbstractIn this paper we consider a new, bio-inspired computing model: the accepting network of spli...
The goal of this paper is to survey, in a uniform and systematic way, the main results regarding net...
This paper presents an extended behavior of networks of evolutionary processors. Usually, such nets ...
In this paper, we introduce generating networks of splicing processors (GNSP for short), a...
This paper presents the model named Accepting Networks of Evolutionary Processors as NP-problem sol...
We consider three complexity classes defined on Accepting Hybrid Networks of Evolutionary Processor...
Parallel genetic algorithms, models and implementations, attempts to exploit the intrinsically paral...
AbstractA hybrid network of evolutionary processors (an HNEP) is a graph where each node is associat...
The Networks of Evolutionary Processors (NEPs) are computing mechanisms directly inspired from the b...
In this paper we simplify a recent model of computation considered in [Margenstern et al. 2005], nam...
* Supported by INTAS 00-626 and TIC 2003-09319-c03-03.This paper presents some connectionist models ...
We consider time complexity classes defined on accepting hybrid networks of evolutionary processors...
The goal of this work is twofold. Firstly, we propose a uniform view of three types of accepting net...
AbstractWe propose a construction of an accepting hybrid network of evolutionary processors (AHNEP) ...
AbstractThe Accepting Networks of Evolutionary Processors (ANEPs for short) are bio-inspired computa...
AbstractIn this paper we consider a new, bio-inspired computing model: the accepting network of spli...
The goal of this paper is to survey, in a uniform and systematic way, the main results regarding net...
This paper presents an extended behavior of networks of evolutionary processors. Usually, such nets ...
In this paper, we introduce generating networks of splicing processors (GNSP for short), a...
This paper presents the model named Accepting Networks of Evolutionary Processors as NP-problem sol...
We consider three complexity classes defined on Accepting Hybrid Networks of Evolutionary Processor...
Parallel genetic algorithms, models and implementations, attempts to exploit the intrinsically paral...
AbstractA hybrid network of evolutionary processors (an HNEP) is a graph where each node is associat...
The Networks of Evolutionary Processors (NEPs) are computing mechanisms directly inspired from the b...
In this paper we simplify a recent model of computation considered in [Margenstern et al. 2005], nam...
* Supported by INTAS 00-626 and TIC 2003-09319-c03-03.This paper presents some connectionist models ...
We consider time complexity classes defined on accepting hybrid networks of evolutionary processors...