Indirectly Encoding Neural Plasticity as a Pattern of Local Rules

@inproceedings{Risi2010IndirectlyEN,
  title={Indirectly Encoding Neural Plasticity as a Pattern of Local Rules},
  author={Sebastian Risi and Kenneth O. Stanley},
  booktitle={Simulation of Adaptive Behavior},
  year={2010}
}
Biological brains can adapt and learn from past experience. [] Key Method Adaptive HyperNEAT is introduced to allow not only patterns of weights across the connectivity of an ANN to be generated by a function of its geometry, but also patterns of arbitrary learning rules. Several such adaptive models with different levels of generality are explored and compared. The long-term promise of the new approach is to evolve large-scale adaptive ANNs, which is a major goal for neuroevolution.

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