Model-based problem solving through symbolic regression via pareto genetic programming

E. Vladislavleva

Research output: ThesisDoctoral Thesis

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Pareto genetic programming methodology is extended by additional generic model selection and generation strategies that (1) drive the modeling engine to creation of models of reduced non-linearity and increased generalization capabilities, and (2) improve the effectiveness of the search for robust models by goal softening and adaptive fitness evaluations. In addition to the new strategies for model development and model selection, this dissertation presents a new approach for analysis, ranking, and compression of given multi-dimensional input-response data for the purpose of balancing the information content of undesigned data sets.
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • Tilburg University
  • den Hertog, Dick, Promotor
  • Smits, G.F., Co-promotor, External person
Award date5 Sept 2008
Place of PublicationTilburg
Print ISBNs9789056682170
Publication statusPublished - 2008


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