  Learning algorithms are being increasingly adopted in various applications.
However, further expansion will require methods that work more automatically.
To enable this level of automation, a more powerful solution representation is
needed. However, by increasing the representation complexity a second problem
arises. The search space becomes huge and therefore an associated scalable and
efficient searching algorithm is also required. To solve both problems, first a
powerful representation is proposed that unifies most of the neural networks
features from the literature into one representation. Secondly, a new diversity
preserving method called Spectrum Diversity is created based on the new concept
of chromosome spectrum that creates a spectrum out of the characteristics and
frequency of alleles in a chromosome. The combination of Spectrum Diversity
with a unified neuron representation enables the algorithm to either surpass or
equal NeuroEvolution of Augmenting Topologies (NEAT) on all of the five classes
of problems tested. Ablation tests justifies the good results, showing the
importance of added new features in the unified neuron representation. Part of
the success is attributed to the novelty-focused evolution and good scalability
with chromosome size provided by Spectrum Diversity. Thus, this study sheds
light on a new representation and diversity preserving mechanism that should
impact algorithms and applications to come.
  To download the code please access the following
https://github.com/zweifel/Physis-Shard.
