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Achievement of the integration-in-manufacturing paradigm based on centralized/distributed hardware/software automation architectures is evolving using the intelligence-in-manufacturing paradigm addressed by IMS industry-led R&D initiatives.
But human society is evolving using only the negative force--the ego.
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First, a type-2 FLC is evolved using Genetic Algorithms (GAs).
The growing film is evolved using kinetic Monte Carlo (KMC) and it is assumed that the film is in mechanical equilibrium.
The population is evolved using a moment method with an interpolative closure employed in the case of a size dependent solids diffusion coefficient.
A system model represented by Linear Graphs is evolved using genetic programming to obtain a design that optimally satisfies a specified level of performance.
The level set field is located at the nodes of the tetrahedral cells and is evolved using new upwind discretizations of Hamilton Jacobi equations combined with a Runge Kutta method for temporal integration.
In the context of element-based FEM-FCT schemes for scalar conservation laws, the numerical solution is evolved using local extremum diminishing (LED) antidiffusive corrections of a low order approximation which is assumed to satisfy the relevant inequality constraints.
These subnetworks are then combined and the final network is evolved using a GA with restricted mutation operator which utilizes the knowledge of the modular structure already generated, for faster convergence.
The methodology follows the principles already employed by the authors in different applications: the NN models are trained by the Levenberg-Marquardt algorithm using a modified training criterion, and the model structure (number of neurons and input terms) is evolved using a Multi-Objective Genetic Algorithm (MOGA).
Figure 16 shows an example evolutionary run in which a 10-node neural network is evolved using the same copying algorithm.
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