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The foundations and implementation of a genetic algorithm (GA) for instrumentation purposes are presented in this paper.
In this paper, we study the implementation of a genetic algorithm (GA) for solving the QoS problem in DVE systems.
Discussion includes the development of a one dimensional plane stress model, disk parameterization methods, and the implementation of a genetic algorithm for shape optimization.
The purpose of this paper is to show a practical implementation of a genetic algorithm for minimizing membrane stresses discrepancies between the actual assembled equilibrium and the specified design state.
In this study, we propose a generic two-promoter genetic regulatory network for the purpose of exhibiting perfect adaptation; our treatment highlights the challenges inherent in the implementation of a genetic integral controller.
To conclude, we present the first implementation of a genetic decoder.
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This situation can be partially resolved with the implementation of a genetic-based second-line drug susceptibility test (29 ).
This paper presents an implementation of a specific genetic algorithm on a high performance computing cluster.
Instead, implementation of a single genetic test is sufficient.
This paper shows the implementation of an elitist genetic algorithm that, when applied to steel structures, is able to obtain structural elements with minimum weight and satisfy the safety factors or coefficients (ultimate limit states) of the applicable building code.
To establish strategies for the implementation of a successful preimplantation genetic diagnosis (PGD) service.
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