Evolving Recurrent Neural Networks for Time Series Data Prediction of Coal Plant Parameters

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Bayesian Neural Architecture Search using A Training-Free Performance Metric (2020, methodology): “Besides NEAT, there are several evolutionary-based approaches to evolve an RNN, such as EXALT [14], EXAMM [32], or a method using ant colony optimization (ACO) to improve LSTM RNNs by refining their cellular structures [15].”
Investigating recurrent neural network memory structures using neuro-evolution (2019, methodology): “NN architecture and cannot evolve an overall RNN structure. III. EVOLUTIONARY EXPLORATION OF AUGMENTING MEMORY MODELS The EXAMM algorithm presented in this work expands on an earlier algorithm, EXALT [26] which can evolve RNNs with either simple neurons or LSTM cells. It further reﬁnes EXALT’s mutat”
Random error sampling-based recurrent neural network architecture optimization (2019, methodology): “Note that the number of epochs of training of the ﬁnal solution was deﬁned considering the experimental design of [37].”
A Survey on Evolutionary Neural Architecture Search (2020, unknown): “objective EA GAs [9], [19]–[23], [33]–[35], [47], [49], [52], [56], [59]–[61], [63], [65]–[67], [69]–[71], [74], [78], [81], [82], [85], [86], [88], [90], [92], [93], [95], [97]– [99], [103], [107], [110], [113], [117]– [119], [121], [122], [125]–[127], [131], [138], [143], [144] [39], [145] [44], [”