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

E. Vladislavleva

Research output: ThesisDoctoral ThesisScientific

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Abstract

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
Supervisors/Advisors
  • den Hertog, Dick, Promotor
  • Smits, G.F., Co-promotor, External person
Award date5 Sep 2008
Place of PublicationTilburg
Publisher
Print ISBNs9789056682170
Publication statusPublished - 2008

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Genetic programming
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Cite this

Vladislavleva, E. (2008). Model-based problem solving through symbolic regression via pareto genetic programming. Tilburg: CentER, Center for Economic Research.
Vladislavleva, E.. / Model-based problem solving through symbolic regression via pareto genetic programming. Tilburg : CentER, Center for Economic Research, 2008. 288 p.
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abstract = "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.",
author = "E. Vladislavleva",
year = "2008",
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series = "CentER Dissertation Series",
publisher = "CentER, Center for Economic Research",
school = "Tilburg University",

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Vladislavleva, E 2008, 'Model-based problem solving through symbolic regression via pareto genetic programming', Doctor of Philosophy, Tilburg University, Tilburg.

Model-based problem solving through symbolic regression via pareto genetic programming. / Vladislavleva, E.

Tilburg : CentER, Center for Economic Research, 2008. 288 p.

Research output: ThesisDoctoral ThesisScientific

TY - THES

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AU - Vladislavleva, E.

PY - 2008

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AB - 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.

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T3 - CentER Dissertation Series

PB - CentER, Center for Economic Research

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Vladislavleva E. Model-based problem solving through symbolic regression via pareto genetic programming. Tilburg: CentER, Center for Economic Research, 2008. 288 p. (CentER Dissertation Series).