On the Locality of Neural Meta-representations

We consider the usage of artificial neural networks for representing genotype-phenotype maps, from and into continuous decision variable domains. Through such an approach, genetic representations become explicitly controllable entities, amenable to adaptation. With a view towards understanding the kinds of space transformations neural networks are able to express, we investigate here the typical representation locality given by arbitrary neuro-encoded genotypephenotype maps. We consistently find high locality space transformations being carried out, across all tested feedforward neural network architectures, in 5, 10 and 30 dimensional spaces.