In pattern recognition area, an ensemble approach is one of promising methods to increase the accuracy of classification systems. It is interesting to use the ensemble approach in evolving game strategies because they maintain a population of solutions simultaneously. Simply, an ensemble is formed from a set of strategies evolved in the last generation. There are many decision factors in the ensemble of game strategies: evolutionary algorithms, fusion methods, and the selection of members in the ensemble. In this paper, several evolutionary algorithms (evolutionary strategy, simple genetic algorithm, fitness sharing, and deterministic crowding algorithm) are compared with three representative fusion methods (majority voting, average, and weighted average) with selective ensembles (compared with the ensemble of all members). Additionally, the computational cost of an exhaustive search for the selective ensemble is reduced by introducing multi-stage evaluations. The ensemble approach is tested on the Othello game with a weight piece counter representation. The proposed ensemble approach outperforms the single best individual from the evolution and ensemble searching time is reasonable.