Evolving a Neurocontroller through a Process of Embryogeny
Diego Federici · The MIT Press eBooks · 2004
We introduce a model of cellular growth that generates neurocontrollers capable of guiding simple simulated agents in a harvesting task. The morphogenesis of the neurocontroller is itself controlled by an evolved artificial neural network. The neural network operates only on local variables and chemical concentrations and is thought as a flexible model of a gene regulatory system and cell metabolism. The model is designed in order to increase the evolvability of the growth mechanism, which constitutes a serious issue in artificial embryogeny. Also, to increase the flexibility of development, organisms are grown in embryonal stages, which allow an incremental refinement of development. Neurocontrollers are organized in horizontal layers, with vertical input and output pathways. Within the same layer, neurons can have only local connections. On one side this limits the information needed for routing and on the other makes the system easy to implement in hardware. Results show that the system is capable of developing appropriate neurocontrollers in most of the evolutionary runs.