Natural Computation Membrane Systems Neural Networks Networks of Evolutionary Processors
Institute of Information Theories and Applications FOI ITHEA
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.
* Supported by INTAS 00-626 and TIC 2003-09319-c03-03.