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:1.8098
- @InProceedings{Mambrini:2014:SMGP,
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author = "Andrea Mambrini and Yang Yu2 and Xin Yao",
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title = "A framework for measuring the generalization ability
of Geometric Semantic Genetic Programming (GSGP) for
Black-Box Boolean Functions Learning",
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booktitle = "Semantic Methods in Genetic Programming",
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year = "2014",
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editor = "Colin Johnson and Krzysztof Krawiec and
Alberto Moraglio and Michael O'Neill",
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address = "Ljubljana, Slovenia",
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month = "13 " # sep,
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note = "Workshop at Parallel Problem Solving from Nature 2014
conference",
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keywords = "genetic algorithms, genetic programming",
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URL = "http://www.cs.put.poznan.pl/kkrawiec/smgp2014/uploads/Site/Mambrini.pdf",
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size = "2 pages",
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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.",
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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