Abstract
Evolutionary algorithms are non-deterministic metaheuristic methods that emulate the evolution of species in nature to solve optimization, search, and learning problems. This article presents a parallel implementation of evolutionary algorithms on Xeon Phi for developing an artificial intelligence to play the NES Pinball game. The proposed parallel implementation offloads the execution of the fitness function evaluation to Xeon Phi. Multiple evolution schemes are studied to get the most efficient resource utilization. A micro-benchmarking of the Xeon Phi coprocessor is performed to verify the existing technical documentation and obtain detail knowledge of its behavior. Finally, a performance analysis of the proposed parallel evolutionary algorithm is presented, focusing on the characteristics of the evaluated platform.
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Rodríguez Leopold, S., Parodi, F., Nesmachnow, S., Mocskos, E. (2017). Evaluation of a Master-Slave Parallel Evolutionary Algorithm Applied to Artificial Intelligence for Games in the Xeon-Phi Many-Core Platform. In: Barrios Hernández, C., Gitler, I., Klapp, J. (eds) High Performance Computing. CARLA 2016. Communications in Computer and Information Science, vol 697. Springer, Cham. https://doi.org/10.1007/978-3-319-57972-6_12
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