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Its operating conditions were then optimized by using genetic algorithm.
The GP model is solved by using genetic algorithm.
Prediction performance of neural network model was optimized by using genetic algorithm.
Moreover, the solution process of optimal sensor placement by using genetic algorithm is clearly demonstrated.
The number of ONUs in the network further reduced by using genetic algorithm.
The results obtained by using genetic algorithm are found better than the conventional optimization algorithm results.
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In this paper, the optimization is achieved by using genetic algorithms.
Essential maintenance actions are considered and optimization is performed by using genetic algorithms.
The optimal model is generated by means of an optimization problem solved by using genetic algorithms.
The design methodology is based on the minimization of a cost function by using genetic algorithms.
This paper proposes a novel approach to spacecraft impulse autonomous rendezvous by using genetic algorithms.
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