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Title: Filtered Networks of Evolutionary Processors
Authors: Fernando de Mingo Lopez, Luis
Santos Menendez, Eugenio
Gisbert, Francisco
Keywords: Natural Computation
Membrane Systems
Neural Networks
Networks of Evolutionary Processors
Issue Date: 2005
Publisher: Institute of Information Theories and Applications FOI ITHEA
Abstract: This paper presents some connectionist models that are widely used to solve NP-problems. Most well known numeric models are Neural Networks that are able to approximate any function or classify any pattern set provided numeric information is injected into the net. Neural Nets usually have a supervised or unsupervised learning stage in order to perform desired response. Concerning symbolic information new research area has been developed, inspired by George Paun, called Membrane Systems. A step forward, in a similar Neural Network architecture, was done to obtain Networks of Evolutionary Processors (NEP). A NEP is a set of processors connected by a graph, each processor only deals with symbolic information using rules. In short, objects in processors can evolve and pass through processors until a stable configuration is reach. This paper just shows some ideas about these two models.
Description: * Supported by INTAS 00-626 and TIC 2003-09319-c03-03.
ISSN: 1313-0463
Appears in Collections:Volume 12 Number 1

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