A Universal Accepting Hybrid Network of Evolutionary Processors

Authors:
Florin Manea;Carlos Martín-Vide;Victor Mitrana
Affiliations:
Faculty of Mathematics and Computer Science, University of Bucharest, Str. Academiei 14, 70109, Bucharest, Romania;Research Group on Mathematical Linguistics, Rovira i Virgili University, Pça Imperial Tàrraco 1, 43005 Tarragona, Spain;Faculty of Mathematics and Computer Science, University of Bucharest, Str. Academiei 14, 70109, Bucharest, Romania and Research Group on Mathematical Linguistics, Rovira i Virgili University, P ...
Venue:
Electronic Notes in Theoretical Computer Science (ENTCS)
Year:
2006

Citing 7
Cited 0

Towards a new architecture for symbolic processing

AIICSR'94 Proceedings of the sixth international conference on Artificial intelligence and information-control systems of robots
Membrane Computing: An Introduction

Membrane Computing: An Introduction
Networks of Parallel Language Processors

New Trends in Formal Languages - Control, Cooperation, and Combinatorics (to Jürgen Dassow on the occasion of his 50th birthday)
Solving NP-Complete Problems With Networks of Evolutionary Processors

IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Connectionist Models of Neurons, Learning Processes and Artificial Intelligence-Part I
On the size complexity of hybrid networks of evolutionary processors

Theoretical Computer Science - Descriptional complexity of formal systems
Hybrid networks of evolutionary processors

GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Solving 3CNF-SAT and HPP in linear time using WWW

MCU'04 Proceedings of the 4th international conference on Machines, Computations, and Universality

Quantified Score

Hi-index	0.00

Visualization

Abstract

We propose a construction of an accepting hybrid network of evolutionary processors (AHNEP) which behaves as a universal device in the class of all these devices. We first construct a Turing machine which can simulate any AHNEP and then an AHNEP which simulates the Turing machine. We think that this approach can be applied to other bio-inspired computing models which are computationally complete.