Neural agents can evolve to reproduce sequences of arbitrary length

We demonstrate that neural agents can evolve behavioral sequences of arbitrary length. In our framework, agents in a two-dimensional arena have to find the secure one among two possible patches, and which of them is secure changes over time. Evolution of arbitrarily long behavioral sequences is achieved by extending the neuroevolution method NEAT with two techniques: Only newly evolved network structure is subject to mutations, and inputs to the neural network are provided in an incremental fashion during evolution. It is suggested that these techniques are transferable to other neuroevolution methods and domains, and constitute a step towards achieving open-ended evolution. Furthermore, it is argued that the proposed techniques are strongly simplified models of processes that to some degree occur naturally in systems with more flexible genetic architectures.