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The semi-automatic or automatic synthesis of robot controller software isboth desirable and challenging. Synthesis of rather simple behaviors such ascollision avoidance by applying artificial evolution has been shown multipletimes. However, the difficulty of this synthesis increases heavily withincreasing complexity of the task that should be performed by the robot. We tryto tackle this problem of complexity with Artificial Homeostatic HormoneSystems (AHHS), which provide both intrinsic, homeostatic processes and(transient) intrinsic, variant behavior. By using AHHS the need for pre-definedcontroller topologies or information about the field of application isminimized. We investigate how the principle design of the controller and thehormone network size affects the overall performance of the artificialevolution (i.e., evolvability). This is done by comparing two variants of AHHSthat show different effects when mutated. We evolve a controller for a robotbuilt from five autonomous, cooperating modules. The desired behavior is a formof gait resulting in fast locomotion by using the modules' main hinges.

Artificial Hormone Reaction Networks: Towards Higher Evolvability in Evolutionary Multi-Modular Robotics