A common genetic representation capable of developing distinct computational architectures

A big challenge in the area of developmental and generative systems, is the design of a method for building complex systems with specific structural and/or functional properties. Most developmental models target specific computational architectures or structures of strictly defined building blocks, in both cases developmental models have strong connection to the target computational architecture/phenotype structure. In this work we seek a common developmental model that can target different architectures but also to find a common genetic representation that can include information that enables such a developmental model. The computational architectures with sparsely connected computational elements considered herein are cellular automata and boolean networks. The experiments study the evolvability of the genetic representation and prove that it is able to build stable structures for distinct computational architectures.