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- W2480452210 abstract "Particle swarm optimization (PSO) has been applied in the past to train neural networks (NN) as evaluation functions for zero-sum board games. The NN weights were adjusted using PSO in a competitive coevolutionary approach. Recent analyses of PSO as a NN training algorithm have revealed a serious issue when bounded activation functions are used in the hidden layer of the NNs: Very early during the training process, activation function saturation occurs, at which point weight adjustments stagnate. This paper studies the effect of activation function stagnation on previously used competitive coevolutionary training of NNs using PSO, and shows that the results reported indicates performance similar to making random game moves, and worse than random moves for ply depths larger than one. New results are presented showing more efficient training of NN game tree evaluation functions when unbounded activation functions are used in the hidden layer, and bounded activation functions in the output layer." @default.
- W2480452210 created "2016-08-23" @default.
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- W2480452210 date "2016-01-01" @default.
- W2480452210 modified "2023-10-14" @default.
- W2480452210 title "An Analysis of Competitive Coevolutionary Particle Swarm Optimizers to Train Neural Network Game Tree Evaluation Functions" @default.
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- W2480452210 doi "https://doi.org/10.1007/978-3-319-41000-5_37" @default.
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