Abstract
Strategies of incremental evolution of artificial neural systems have been suggested over the last decade to overcome the scalability problem of evolutionary robotics. In this article two methods are introduced that support the evolution of neural couplings and extensions of recurrent neural networks of general type. These two methods are applied to combine and extend already evolved behavioral functionality of an autonomous robot in order to compare the structure-function relations of the resulting networks with those of the initial structures. The results of these investigations indicate that the emergent dynamics of the resulting networks turn these control structures into irreducible systems. We will argue that this leads to several consequences. One is, that the scalability problem of evolutionary robotics remains unsolved, no matter which type of incremental evolution is applied.
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Hülse, M., Pasemann, F. (2006). Modular Design of Irreducible Systems. In: Nolfi, S., et al. From Animals to Animats 9. SAB 2006. Lecture Notes in Computer Science(), vol 4095. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11840541_44
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DOI: https://doi.org/10.1007/11840541_44
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