  Game-playing Evolutionary Algorithms, specifically Rolling Horizon
Evolutionary Algorithms, have recently managed to beat the state of the art in
win rate across many video games. However, the best results in a game are
highly dependent on the specific configuration of modifications and hybrids
introduced over several papers, each adding additional parameters to the core
algorithm. Further, the best previously published parameters have been found
from only a few human-picked combinations, as the possibility space has grown
beyond exhaustive search. This paper presents the state of the art in Rolling
Horizon Evolutionary Algorithms, combining all modifications described in
literature, as well as new ones, for a large resultant hybrid. We then use a
parameter optimiser, the N-Tuple Bandit Evolutionary Algorithm, to find the
best combination of parameters in 20 games from the General Video Game AI
Framework. Further, we analyse the algorithm's parameters and some interesting
combinations revealed through the optimisation process. Lastly, we find new
state of the art solutions on several games by automatically exploring the
large parameter space of RHEA.
