A framework for measuring the generalization ability of Geometric Semantic Genetic Programming (GSGP) for Black-Box Boolean Functions Learning

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Mambrini:2014:SMGP,

• author = "Andrea Mambrini and Yang Yu2 and Xin Yao",
• title = "A framework for measuring the generalization ability of Geometric Semantic Genetic Programming (GSGP) for Black-Box Boolean Functions Learning",
• booktitle = "Semantic Methods in Genetic Programming",
• year = "2014",
• editor = "Colin Johnson and Krzysztof Krawiec and Alberto Moraglio and Michael O'Neill",
• address = "Ljubljana, Slovenia",
• month = "13 " # sep,
• note = "Workshop at Parallel Problem Solving from Nature 2014 conference",
• keywords = "genetic algorithms, genetic programming",
• URL = "http://www.cs.put.poznan.pl/kkrawiec/smgp2014/uploads/Site/Mambrini.pdf",
• size = "2 pages",
• abstract = "Moraglio et al. proposed GSGP operators for learning Boolean functions [1]. The work provides upper bounds of the expected time for the algorithm to t the training set but it doesn't give any guarantees on how the learnt functions will evolve on unseen input. In this work we provide a framework to analyse GSGP as learning tool. This can be used to obtain lower bounds on the generalisation error of the Boolean functions evolved by the algorithm.",
• notes = "SMGP 2014 http://www.cs.put.poznan.pl/kkrawiec/smgp/?n=Site.SMGP2014",

}

Genetic Programming entries for Andrea Mambrini Yang Yu2 Xin Yao

Citations