  The complexity of a legged robot's environment or task can inform how
specialised its gait must be to ensure success. Evolving specialised robotic
gaits demands many evaluations - acceptable for computer simulations, but not
for physical robots. For some tasks, a more general gait, with lower
optimization costs, could be satisfactory. In this paper, we introduce a new
type of gait controller where complexity can be set by a single parameter,
using a dynamic genotype-phenotype mapping. Low controller complexity leads to
conservative gaits, while higher complexity allows more sophistication and high
performance for demanding tasks, at the cost of optimization effort. We
investigate the new controller on a virtual robot in simulations and do
preliminary testing on a real-world robot. We show that having variable
complexity allows us to adapt to different optimization budgets. With a high
evaluation budget in simulation, a complex controller performs best. Moreover,
real-world evolution with a limited evaluation budget indicates that a lower
gait complexity is preferable for a relatively simple environment.
