evolution of multi-loop controllers for fixed morphology with a cyclic genetic algorithm
Cyclic genetic algorithms can be used to generate single loop control programs for robots. While successful in generating controllers for individual leg movement, gait generation, and area search path finding, cyclic genetic algorithms have had limited use when dealing with control problems that require different behaviors in response to sensor inputs. For such behaviors, there is a need for modifications that will allow the generation of multi-loop control programs, which can properly react to sensor input. In this work, we present modifications to the standard cyclic genetic algorithm that enables it to learn multi-loop control programs with branching that allows the control to jump from one loop to another. Preliminary tests show the success of our modification.
control learning evolutionary robotics code generation