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The diffusion reaction equations for different model versions have been solved using a finite-differencing technique.
The problem has been solved using a new hybrid evolutionary algorithm.
The complete optimization problem has been solved using a novel algorithm, inspired by evolutionary and social behavior metaphors.
The inherent problem of training CRF with partially labeled data has been solved using a novel parameter estimation algorithm.
The two-dimensional (2-D) field equations have been solved using a finite volume method based solver (FLUENT 6.3).
Within the framework of such a cell model, the momentum and energy equations have been solved using a finite difference method to obtain the velocity and temperature fields.
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This model is solved using a computationally efficient genetic algorithm.
These aspects are solved using a multi-cellular genetic algorithm.
The problem is solved using a multi-objective evolutionary algorithm.
The equations were solved using a Matlab procedure.
The differential equations are solved using a power series approach.
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