Evolving neural network structures using axonal growth mechanisms

In the field of artificial evolution creating methods to evolve neural networks is an important goal. But how to encode the structure and properties of the neural network in the genome is still a problem. If one overloads the genome with detailed information for a network the evolutionary time increases prohibitively. If the genome is too simple, only simple problems can be solved. As Nature has found an efficient and evolvable solution to this problem, it is worthwhile imitating the mechanisms on how biological neural nets are generated. In this paper I propose a model in which artificial genes tune the ability of axons to find, detect and connect to specific targets. Initial simulation results of simple tasks are evolved and the genetic tuning of the developmental processes for artificial evolution is discussed