Using cyclic genetic algorithms to evolve multi-loop control programs

Cyclic genetic algorithms were developed to evolve single loop control programs for robots. These programs have been used for three levels of control: individual leg movement, gait generation, and area search path finding. In all of these applications the cyclic genetic algorithm learned the cycle of actuator activations that could be continually repeated to produce the desired behavior. Although very successful for these applications, it was not applicable to control problems that required different behaviors in response to sensor inputs. Control programs for this type of behavior require multiple loops with conditional statements to regulate the branching. In this paper, we present modifications to the standard cyclic genetic algorithm that allow it to learn multi-loop control programs that can react to sensor input.