This thesis proposes several measures for a better understanding of the dynamic of the evolutionary process. In particular, measures to control bloat, overfitting and complexity of the solutions are defined. Moreover, several methods to improve the generalization ability of the solutions produced by the standard Genetic Programming (GP) algorithm are proposed. This thesis also focuses on the role of semantic in GP. In particular, a new GP algorithm that uses only semantic information is proposed. Finally, a study on the effect of multi objective optimization in GP is performed.

(2012). Measures and methods for robust genetic programming. (Tesi di dottorato, Università degli Studi di Milano-Bicocca, 2012).

Measures and methods for robust genetic programming

CASTELLI, MAURO
2012

Abstract

This thesis proposes several measures for a better understanding of the dynamic of the evolutionary process. In particular, measures to control bloat, overfitting and complexity of the solutions are defined. Moreover, several methods to improve the generalization ability of the solutions produced by the standard Genetic Programming (GP) algorithm are proposed. This thesis also focuses on the role of semantic in GP. In particular, a new GP algorithm that uses only semantic information is proposed. Finally, a study on the effect of multi objective optimization in GP is performed.
VANNESCHI, LEONARDO
SILVA, SARA
Genetic Programming; Measures; Semantic; Generalization; Multi Objective Optimization
INF/01 - INFORMATICA
English
Scuola di dottorato di Scienze
INFORMATICA - 22R
24
2010/2011
(2012). Measures and methods for robust genetic programming. (Tesi di dottorato, Università degli Studi di Milano-Bicocca, 2012).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/28571
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