An Initialization Method for Grammatical Evolution Assisted by Decision Trees
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Russo:2016:CEC,
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author = "Igor L. S. Russo and Heder S. Bernardino and
Carlos C. H. Borges and Helio J. C. Barbosa",
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title = "An Initialization Method for Grammatical Evolution
Assisted by Decision Trees",
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booktitle = "Proceedings of 2016 IEEE Congress on Evolutionary
Computation (CEC 2016)",
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year = "2016",
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editor = "Yew-Soon Ong",
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pages = "3300--3307",
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address = "Vancouver",
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month = "24-29 " # jul,
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publisher = "IEEE Press",
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keywords = "genetic algorithms, genetic programming, Grammatical
Evolution",
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isbn13 = "978-1-5090-0623-6",
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DOI = "doi:10.1109/CEC.2016.7744207",
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abstract = "Grammatical Evolution (GE) is a genetic programming
technique in which the candidate solutions are
represented using a binary genotype and the programs
can be generated through production rules of a formal
grammar. Similarly to other evolutionary computation
methods, the GE's performance can be improved when an
adequate initial population seeding is adopted.
Decision trees are widely used to model classifiers in
machine learning and their symbolic form can be mapped
back to the GE's binary representation of the candidate
individuals. Thus, the use of machine learning
techniques to generate decision trees to compose the
initial population of GE is investigated here.
Computational experiments with a real world data set
are carried out and the results show an increase of
performance when compared to the traditional seeding
approach.",
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notes = "WCCI2016",
- }
Genetic Programming entries for
Igor L S Russo
Heder Soares Bernardino
Carlos Cristiano Hasenclever Borges
Helio J C Barbosa
Citations