Reducing Dimensionality to Improve Search in Semantic Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Oliveira:2016:PPSN,
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author = "Luiz Otavio V. B. Oliveira and
Luis Fernando Miranda and Gisele L. Pappa and Fernando E. B. Otero and
Ricardo H. C. Takahashi",
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title = "Reducing Dimensionality to Improve Search in Semantic
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 = "375--385",
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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,
Dimensionality reduction, Instance selection",
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isbn13 = "978-3-319-45823-6",
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URL = "https://kar.kent.ac.uk/id/eprint/56211",
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URL = "https://core.ac.uk/download/pdf/42412374.pdf",
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DOI = "doi:10.1007/978-3-319-45823-6_35",
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size = "11 pages",
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abstract = "Genetic programming approaches are moving from
analysing the syntax of individual solutions to look
into their semantics. One of the common definitions of
the semantic space in the context of symbolic
regression is a n-dimensional space, where n
corresponds to the number of training examples. In
problems where this number is high, the search process
can became harder as the number of dimensions increase.
Geometric semantic genetic programming (GSGP) explores
the semantic space by performing geometric semantic
operations, the fitness landscape seen by GSGP is
guaranteed to be conic by construction. Intuitively, a
lower number of dimensions can make search more
feasible in this scenario, decreasing the chances of
data overfitting and reducing the number of evaluations
required to find a suitable solution. This paper
proposes two approaches for dimensionality reduction in
GSGP: (i) to apply current instance selection methods
as a pre-process step before training points are given
to GSGP; (ii) to incorporate instance selection to the
evolution of GSGP. Experiments in 15 datasets show that
GSGP performance is improved by using instance
reduction during the evolution.",
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notes = "p382 'TCNN and TENN ... no better than ... a random
selection'
PPSN2016",
- }
Genetic Programming entries for
Luiz Otavio Vilas Boas Oliveira
Luis Fernando Miranda
Gisele L Pappa
Fernando Esteban Barril Otero
Ricardo Hiroshi Caldeira Takahashi
Citations