Selection of Neural Architecture and the Environment Complexity

In this paper we consider how the complexity of evolved neural controllers depends on the environment using foraging behavior of the Cyber Rodent in two different environments. In the first environment, each fruit can be seen from limited directions and different groups of fruits become ripe in different periods. In the second environment, fruits inside a zone are rewarding and those outside are aversive. After evolution, agents with recurrent neural controller outperformed those with feed-forward controllers by effectively using the memory of border passage. Simulation and experimental results with the Cyber Rodent robot confirmed the selection of appropriate complexity of neural controller, both in size and structure, through evolution.