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Particle swarm optimization is used to optimize the model parameters.
In order to optimize the model performance, a recently developed optimization technique based on rate rules (Cai and Pitsch, 2014) is extended in this study.
In particular, when the realized asset returns are less than the estimates used to optimize the model, the realized portfolio return will be less than the optimal portfolio return given by the objective.
The user does not need to optimize the model parameters.
The data selection process was set to optimize the model time resolution.
Genetic algorithm and the conjugated gradient method are combined to optimize the model parameters.
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Moreover, an information criterion is used during the first phase to optimize the models in terms of accuracy and complexity.
We optimize the models using data from wild type and four single receptor mutant experiments and use data from two receptor double mutant experiments in a validation step.
EIS data for the passive state are interpreted in terms of the PDM by optimizing the model on the data using commercial optimization program.
In our analysis we found an optimal distance between a modification to a target gene based on optimizing the model's performance.
Mechanistic design optimizes the model at the collaboration level.
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