Exploring different coding schemes for the evolution of an artificial insect eye

The existing literature proposes various (neuronal) architectures for object avoidance, which is one of the very fundamental tasks of autonomous, mobile robots. Due to certain hardware limitations, existing research resorts to prespecified sensor systems that remain fixed during all experiments, and modifications are done only in the controllers' software components. Only recent research (Lichtensteiger and Eggenberger, 1999) has tried to do the opposite, i.e., prespecifying a simple neural network and evolving the sensor distribution directly in hardware. Even though first experiments have been successful in evolving some solutions by means of evolutionary algorithms, they have also indicated that systematic comparisons between different evolutionary algorithms and codings schemes are required in order to optimize the evolutionary process. Since these comparisons cannot be done on the robot due to experimentation time, this paper reports the result of a set of extensive simulations.