Excerpt from PLoS Biology:
These examples of experimental evolution with robots verify the power of evolution by mutation, recombination, and natural selection. In all cases, robots initially exhibited completely uncoordinated behaviour because their genomes had random values. However, a few hundreds of generations of random mutations and selective reproduction were sufficient to promote the evolution of efficient behaviours in a wide range of environmental conditions. The ability of robots to orientate, escape predators, and even cooperate is particularly remarkable given that they had deliberately simple genotypes directly mapped into the connection weights of neural networks comprising only a few dozen neurons.
So far, evolutionary robotic experiments have been conducted mostly by computer scientists and engineers. Their primary interest has been to exploit the power of artificial evolution to automatically generate novel or better control systems and body shapes for specific problems. For example, the method of evolutionary robotics described in the context of cooperative behaviour has been successfully used to generate the control systems of a swarm of micro aerial vehicles that must locate rescuers and spread so as to establish a radio communication network based uniquely on signal strength of the rescuer mobile phones and of the robot emitters, a problem for which existing engineering solutions require the use of absolute geo-localisation information provided by GPS signals .
The use of real robot features are particularly useful in an evolutionary perspective where behaviour and ensuing complex physical interactions can significantly affect the interaction with the environment and performance. Therefore, evolutionary robotics also offers new opportunities to address issues such as sexual selection, division of labour, speciation, and, in general, the open-ended evolution of diversity and complexity in behavioural systems.