Grammatical Evolution Using Tree Representation Learning
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @InProceedings{conf/iconip/MarutaZNSK17,
-
author = "Shunya Maruta and Yi Zuo and Masahiro Nagao and
Hideyuki Sugiura and Eisuke Kita",
-
title = "Grammatical Evolution Using Tree Representation
Learning",
-
booktitle = "Neural Information Processing - 24th International
Conference, ICONIP 2017, Guangzhou, China, November
14-18, 2017, Proceedings, Part IV",
-
editor = "Derong Liu and Shengli Xie and Yuanqing Li and
Dongbin Zhao and El-Sayed M. El-Alfy",
-
publisher = "Springer",
-
year = "2017",
-
pages = "346--355",
-
series = "Lecture Notes in Computer Science",
-
volume = "10637",
-
keywords = "genetic algorithms, genetic programming, grammatical
evolution, tree representation, multiple chromosomes,
pointer allocation, genotype-phenotype map",
-
bibdate = "2017-11-17",
-
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/iconip/iconip2017-4.html#MarutaZNSK17",
-
DOI = "doi:10.1007/978-3-319-70093-9_36",
-
isbn13 = "978-3-319-70092-2",
-
abstract = "Grammatical evolution (GE) is one of the evolutionary
computations, which evolves genotype to map phenotype
by using the Backus-Naur Form (BNF) syntax. GE has been
widely employed to represent syntactic structure of a
function or a program in order to satisfy the design
objective. As the GE decoding process parses the
genotype chromosome into array or list structures with
left-order traversal, encoding process could change
gene codons or orders after genetic operations. For
improving this issue, this paper proposes a novel GE
algorithm using tree representation learning (GETRL)
and presents three contributions to the original GE,
genetic algorithm (GA) and genetic programming (GP).
Firstly, GETRL uses a tree-based structure to represent
the functions and programs for practical problems. To
be different from the traditional GA, GETRL adopts a
genotype-to-phenotype encoding process, which
transforms the genes structures for tree traversal.
Secondly, a pointer allocation mechanism is introduced
in this method, which allows the GETRL to pursue the
genetic operations like typical GAs. To compare with
the typical GP, however GETRL still generates a tree
structure, our method adopts a phenotype-to-genotype
decoding process, which allows the genetic operations
be able to be apply into tree-based structure. Thirdly,
due to each codon in GE has different expression
meaning, genetic operations are quite different from
GAs, in which all codons have the same meaning. In this
study, we also suggest a multi-chromosome system and
apply it into GETRL, which can prevent from overriding
the codons for different objectives.",
- }
Genetic Programming entries for
Shunya Maruta
Yi Zuo
Masahiro Nagao
Hideyuki Sugiura
Eisuke Kita
Citations