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Ph. D. ThesisPh. D. Thesis 7. Results – Genetic Algorithm Framework7. Results – Genetic Algorithm Framework
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Ph. D. Thesis
  Abstract
  Table of Contents
  1. Introduction
  2. Theory – Fundamentals of the Multivariate Data Analysis
  3. Theory – Quantification of the Refrigerants R22 and R134a: Part I
  4. Experiments, Setups and Data Sets
  5. Results – Kinetic Measurements
  6. Results – Multivariate Calibrations
  7. Results – Genetic Algorithm Framework
    7.1. Single Run Genetic Algorithm
    7.2. Genetic Algorithm Framework - Theory
    7.3. Genetic Algorithm Framework - Results
    7.4. Genetic Algorithm Framework – Conclusions
  8. Results – Growing Neural Network Framework
  9. Results – All Data Sets
  10. Results – Various Aspects of the Frameworks and Measurements
  11. Summary and Outlook
  12. References
  13. Acknowledgements
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7.    Results – Genetic Algorithm Framework

In the previous chapter, it was demonstrated that neural networks show the best calibration for the refrigerant data set due to the nonlinearities present in the data. It was also shown that a compression of the input variables by a simple combination of a PCA and neural networks shows results comparable with the neural networks using all variables. Therefore, it is expected that a more sophisticated selection of the input variables can improve the generaliza­tion ability and thus the calibration quality. Hence, a genetic algorithm for the variable selection is combined with neural networks for the calibration in this chapter. As single applications of this combination neither show superior calibrations nor repro­ducible variable selections, a framework is setup, which uses many parallel runs of the genetic algorithm for different data subsets resulting in improved calibrations and a high reproducibility. 

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