Evolving Regular Neural Networks

Combining evolution with concepts from developmental biology produces repeated patterns in neural networks that increase intelligence. Such repeated patterns are evidence of a property called "regularity", which typically involves symmetries and the repetition of design themes, with and without variation. Our research has shown how you can evolve regular phenotypes--whether they be neural networks, robot morphologies, or 3-D printable shapes--and that evolving such regularity improves performance and evolvability. We evolve such regularity with a generative encoding based on developmental biology called a compositional pattern producing network (CPPN), which is also used to evolve neural networks in the HyperNEAT algorithm.

Image: Evolved neural network robot controllers that have different regular patterns of neural connectivity.