Feature Extraction for Surrogate Models in Genetic Programming
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- @InProceedings{Pilat:2016:PPSN,
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author = "Martin Pilat and Roman Neruda",
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title = "Feature Extraction for Surrogate Models in Genetic
Programming",
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booktitle = "14th International Conference on Parallel Problem
Solving from Nature",
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year = "2016",
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editor = "Julia Handl and Emma Hart and Peter R. Lewis and
Manuel Lopez-Ibanez and Gabriela Ochoa and
Ben Paechter",
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volume = "9921",
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series = "LNCS",
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pages = "335--344",
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address = "Edinburgh",
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month = "17-21 " # sep,
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming, Surrogate
model, Random forest",
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isbn13 = "978-3-319-45823-6",
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DOI = "doi:10.1007/978-3-319-45823-6_31",
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abstract = "We discuss the use of surrogate models in the field of
genetic programming. We describe a set of features
extracted from each tree and use it to train a model of
the fitness function. The results indicate that such a
model can be used to predict the fitness of new
individuals without the need to evaluate them. In a
series of experiments, we show how surrogate modelling
is able to reduce the number of fitness evaluations
needed in genetic programming, and we discuss how the
use of surrogate models affects the exploration and
convergence of genetic programming algorithms.",
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notes = "PPSN2016",
- }
Genetic Programming entries for
Martin Pilat
Roman Neruda
Citations