Graph structured program evolution

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{1277290,
  author =       "Shinichi Shirakawa and Shintaro Ogino and 
                 Tomoharu Nagao",
  title =        "Graph structured program evolution",
  booktitle =    "GECCO '07: Proceedings of the 9th annual conference on
                 Genetic and evolutionary computation",
  year =         "2007",
  editor =       "Dirk Thierens and Hans-Georg Beyer and 
                 Josh Bongard and Jurgen Branke and John Andrew Clark and 
                 Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and 
                 Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and 
                 Julian F. Miller and Jason Moore and Frank Neumann and 
                 Martin Pelikan and Riccardo Poli and Kumara Sastry and 
                 Kenneth Owen Stanley and Thomas Stutzle and 
                 Richard A Watson and Ingo Wegener",
  volume =       "2",
  isbn13 =       "978-1-59593-697-4",
  pages =        "1686--1693",
  address =      "London",
  URL =          "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p1686.pdf",
  DOI =          "doi:10.1145/1276958.1277290",
  publisher =    "ACM Press",
  publisher_address = "New York, NY, USA",
  month =        "7-11 " # jul,
  organisation = "ACM SIGEVO (formerly ISGEC)",
  keywords =     "genetic algorithms, genetic programming, automatic
                 programming, exponentiation, factorial, Fibonacci
                 sequence, genetic algorithms, graph based genetic
                 programming, reversing a list",
  size =         "8 pages",
  abstract =     "In recent years a lot of Automatic Programming
                 techniques have developed. A typical example of
                 Automatic Programming is Genetic Programming (GP), and
                 various extensions and representations for GP have been
                 proposed so far. However, it seems that more
                 improvements are necessary to obtain complex programs
                 automatically. In this paper we proposed a new method
                 called Graph Structured Program Evolution (GRAPE). The
                 representation of GRAPE is graph structure, therefore
                 it can represent complex programs (e.g. branches and
                 loops) using its graph structure. Each program is
                 constructed as an arbitrary directed graph of nodes and
                 data set. The GRAPE program handles multiple data types
                 using the data set for each type, and the genotype of
                 GRAPE is the form of a linear string of integers. We
                 apply GRAPE to four test problems, factorial, Fibonacci
                 sequence, exponentiation and reversing a list, and
                 demonstrate that the optimum solution in each problem
                 is obtained by the GRAPE system.",
  notes =        "GECCO-2007 A joint meeting of the sixteenth
                 international conference on genetic algorithms
                 (ICGA-2007) and the twelfth annual genetic programming
                 conference (GP-2007).

                 ACM Order Number 910071

                 Multiple data types. Graph may have several output
                 nodes. Genotype fixed length (linear!) vector of
                 integers. Genotype-phenotype.",
}

Genetic Programming entries for Shinichi Shirakawa Shintaro Ogino Tomoharu Nagao

Citations