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Multi-stage Genetic Algorithm and Deep Neural Network for Robot Execution Failure Detection

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Abstract

In this paper, we propose a multi-stage genetic algorithm that allows to automatically initialize deep multilayer perceptron neural network models to train it for prediction of robot execution failures. The proposed genetic algorithm system is divided on three stages, the first stage consists of initializing number of hidden layers. The second stage aims to fix number of neurons in each hidden layer. The final stage generates the activation function and the optimizer used to train neural network models. The next step is the application of the generated neural network models to predict robot execution failures. The aim of this approach is giving a robot many models so it can better take a more precise decision, since there is no scientific method to choose neural network model, genetic algorithm allows to generate many models automatically. Results obtained in this study show the efficiency of deep neural networks on robotic failures detection, as well as the efficiency of genetic algorithms to generate different models automatically which prevent the manual setup.

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Notes

  1. http://archive.ics.uci.edu/ml/datasets/Robot+Execution+Failures.

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Authors and Affiliations

  1. GeCoDe Laboratory, Department of Computer Science, University Dr. Tahar Moulay of Saida, Saida, Algeria

    Mohamed Elhadi Rahmani & Abdelmalek Amine

  2. Instituto Politécnico de Bragança, Bragança, Portugal

    José Eduardo Fernandes

Authors
  1. Mohamed Elhadi Rahmani
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  2. Abdelmalek Amine
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  3. José Eduardo Fernandes
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Correspondence to Mohamed Elhadi Rahmani.

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Rahmani, M.E., Amine, A. & Fernandes, J.E. Multi-stage Genetic Algorithm and Deep Neural Network for Robot Execution Failure Detection. Neural Process Lett 53, 4527–4547 (2021). https://doi.org/10.1007/s11063-021-10610-x

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  • DOI: https://doi.org/10.1007/s11063-021-10610-x

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