Grammatical Evolution Using Tree Representation Learning

Created by W.Langdon from gp-bibliography.bib Revision:1.4448

@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