Cyclic genetic algorithms for evolving multi-loop control programs

The cyclic genetic algorithm (CGA) has proven to be an effective method for evolving single loop control programs such as ones used for gait generation. The current limitation of the CGA is that it does not allow for conditional branching or a multi-loop program, which is required to integrate sensor input. In this work, we extend the capabilities of the CGA to evolve the program for a controller that incorporates sensors. To test our new method, we chose to evolve a robot in simulation that is capable of efficiently finding a stationary target