Abstract
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.
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Silva, A., Neves, A., Costa, E. (2000). Polymorphy and Hybridization in Genetically Programmed Networks. In: Schoenauer, M., et al. Parallel Problem Solving from Nature PPSN VI. PPSN 2000. Lecture Notes in Computer Science, vol 1917. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45356-3_22
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DOI: https://doi.org/10.1007/3-540-45356-3_22
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