Abstract—The production of renewable and sustainable energy
is one of the most important challenges currently facing mankind.
Wind has made an increasing contribution to the world’s energy
supply mix, but remains a long way from reaching its full potential.
In this paper, we investigate the use of artificial evolution to
design vertical-axis wind turbine prototypes that are physically
instantiated and evaluated under approximated wind tunnel conditions.
Initially, a conventional evolutionary algorithm is used
to explore the design space of a single wind turbine and later
a cooperative coevolutionary algorithm is used to explore the
design space of an array of wind turbines. Artificial neural networks
are used throughout as surrogate models to assist learning
and found to reduce the number of fabrications required to reach
a higher aerodynamic efficiency. Unlike other approaches, such
as computational fluid dynamics simulations, no mathematical
formulations are used and no model assumptions are made.