Evolving neural networks through augmenting topologies
Evolutionary Computation
Proceedings of the second Australasian conference on Interactive entertainment
Real-time neuroevolution in the NERO video game
IEEE Transactions on Evolutionary Computation
An Evolutionary Algorithm Approach to Optimal Ensemble Classifiers for DNA Microarray Data Analysis
IEEE Transactions on Evolutionary Computation
Optimizing strategy parameters in a game bot
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
Neuroevolution based multi-agent system for micromanagement in real-time strategy games
Proceedings of the Fifth Balkan Conference in Informatics
Evolving the strategies of agents for the ANTS game
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advences in computational intelligence - Volume Part II
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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.