Weighting NTBEA for Game AI Optimisation
  The N-Tuple Bandit Evolutionary Algorithm (NTBEA) has proven very effective
in optimising algorithm parameters in Game AI. A potential weakness is the use
of a simple average of all component Tuples in the model. This study
investigates a refinement to the N-Tuple model used in NTBEA by weighting these
component Tuples by their level of information and specificity of match. We
introduce weighting functions to the model to obtain Weighted- NTBEA and test
this on four benchmark functions and two game environments. These tests show
that vanilla NTBEA is the most reliable and performant of the algorithms
tested. Furthermore we show that given an iteration budget it is better to
execute several independent NTBEA runs, and use part of the budget to find the
best recommendation from these runs.
