  This paper introduces a speciation principle for neuroevolution where
evolving networks are grouped into species based on the number of hidden
neurons, which is indicative of the complexity of the search space. This
speciation principle is indivisibly coupled with a novel genotype
representation which is characterised by zero genome redundancy, high
resilience to bloat, explicit marking of recurrent connections, as well as an
efficient and reproducible stack-based evaluation procedure for networks with
arbitrary topology. Furthermore, the proposed speciation principle is employed
in several techniques designed to promote and preserve diversity within species
and in the ecosystem as a whole. The competitive performance of the proposed
framework, named Cortex, is demonstrated through experiments. A highly
customisable software platform which implements the concepts proposed in this
study is also introduced in the hope that it will serve as a useful and
reliable tool for experimentation in the field of neuroevolution.

Neural and Evolutionary Computing;Artificial Intelligence