Grammatical evolution using two-dimensional gene for symbolic regression: an advanced improvement with conditional statement grammar
Created by W.Langdon from
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- @Article{journals/ijcistudies/SugiuraNZK16,
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title = "Grammatical evolution using two-dimensional gene for
symbolic regression: an advanced improvement with
conditional statement grammar",
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author = "Hideyuki Sugiura and Masahiro Nagao and Yi Zuo and
Eisuke Kita",
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journal = "International Journal of Computational Intelligence
Studies",
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year = "2016",
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number = "3/4",
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volume = "5",
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pages = "237--251",
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keywords = "genetic algorithms, genetic programming, grammatical
evolution",
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bibsource = "http://dblp.uni-trier.de/db/journals/ijcistudies/ijcistudies5.html#SugiuraNZK16",
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DOI = "doi:10.1504/IJCISTUDIES.2016.10004364",
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abstract = "Symbolic regression problems can be solved using
grammatical evolution (GE), an evolutionary computation
(EC) method, to find a function that coincides
satisfactorily with the given datasets. The evolutional
approach of GE is based on the grammar learning
paradigm, which can translate the genotype (binary
digit) into the phenotype (terminals and
non-terminals). Unlike traditional codons in a
genotype, the fittest codons in phenotype represented
by the Backus-Naur form (BNF) are difficult for next
generation genes to inherit the traits of parents,
accounting for crossover and mutation. For this issue,
this article presents a proposal of an advanced
improvement to GE using a two-dimensional gene (GE2DG).
In contrast to multi-chromosomal GE (GEMC), our
proposal not only encloses the two-dimensional
gene-expression for symbolic regression, but also
introduces one independent gene defined as a
conditional statement to express a new BNF grammar of
an if-then (-else) branch. In the experiments described
herein, continuous/discontinuous non-branch functions
and continuous/discontinuous branch functions, four
testing patterns, are considered as numerical examples.
Results show that GE2DG has better performance than the
original GE or GEMC. Especially for the case of branch
functions, GE with hybrid chromosome (GEHC), where
GE2DG is incorporated with GEMC, has faster convergence
in symbolic regression than other methods.",
-
notes = "IJCIStudies",
- }
Genetic Programming entries for
Hideyuki Sugiura
Masahiro Nagao
Yi Zuo
Eisuke Kita
Citations