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. 2023 Feb 3;23(3):1713. doi: 10.3390/s23031713
Algorithm 1 GA-based NAS Algorithm
  • 1:

      Input: the number of generations T, the number of individuals in each generation N,

    the dataset D;

  • 2:

      Initialization: generating the initial individuals randomly;

  • 3:

      For 1,2,3, …, T do;

  • 4:

      For 1,2,3, …, N do;

  • 5:

      Evaluation: neural network architecture (individual) training for D_train;

  • 6:

      Evaluate the performance of the neural network architecture for D_test;

  • 7:

      end for

  • 8:

      Selection: selecting the most suitable individuals for reproduction;

  • 9:

      Crossover and Mutation: reproducing new individuals;

  • 10:

    end for

  • 11:

    Output: individuals in the last generation.