Polymorphy and Hybridization in Genetically Programmed Networks



In this paper we discuss the polymorphic abilities of a new distributed
representation for genetic programming, called Genetically Programmed
Networks. These are inspired in a common structure in natural complex adaptive
systems, where system functionality frequently emerges from the combined
functionality of simple computational entities, densely interconnected for information
exchange. A Genetically Programmed Network can be evolved into a
distributed program, a rule based system or a neural network with simple adjustments
to the evolutionary algorithm. The space of possible network topologies
can also be easily controlled. This allows the fast exploration of various
search spaces thus increasing the possibility of finding a (or a better) solution.
Experimental results are presented to support our claims.


Genetic Programming


Genetic Programming

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