In the real-time strategy game, success of AI depends on consecutive and effective decision making on actions by NPCs in the game. In this regard, there have been many researchers to find the optimized choice. This paper confirms the improvement of NPC performance in a real-time strategy game by using the speciated evolutionary algorithm for such decision making on actions, which has been largely applied to the classification problems. Creation and selection of members to use for this ensemble method is manifested through speciation and the performance is verified through 'conqueror', a real-time strategy game platform developed by our previous work.
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