  Monte Carlo Tree Search techniques have generally dominated General Video
Game Playing, but recent research has started looking at Evolutionary
Algorithms and their potential at matching Tree Search level of play or even
outperforming these methods. Online or Rolling Horizon Evolution is one of the
options available to evolve sequences of actions for planning in General Video
Game Playing, but no research has been done up to date that explores the
capabilities of the vanilla version of this algorithm in multiple games. This
study aims to critically analyse the different configurations regarding
population size and individual length in a set of 20 games from the General
Video Game AI corpus. Distinctions are made between deterministic and
stochastic games, and the implications of using superior time budgets are
studied. Results show that there is scope for the use of these techniques,
which in some configurations outperform Monte Carlo Tree Search, and also
suggest that further research in these methods could boost their performance.
