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For trapezoidal and normal fuzzy travel times, we first turn the original VaR p-hub center problem into its equivalent parametric mixed-integer programming problem, then develop a hybrid algorithm by incorporating genetic algorithm and local search (GALS) to solve the parametric mixed-integer programming problem.
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Therefore, in the proposed work, a neuro-genetic algorithm has been designed and implemented by incorporating a genetic algorithm (GA) component into the ANN in the training phase.
Eerr is minimized by the genetic algorithm.
They solved their model by using genetic algorithm.
Prediction performance of neural network model was optimized by using genetic algorithm.
The optimal combination of the weights is derived by a genetic algorithm.
The problem is solved by a genetic algorithm.
Then, the optimal thresholds are obtained by the genetic algorithm.
Its operating conditions were then optimized by using genetic algorithm.
The GP model is solved by using genetic algorithm.
The MLP has been trained by a genetic algorithm (GA) and a Levenberg-Marquardt learning algorithm.
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