Visualizing Genetic Programming Ancestries Using Graph Databases

Created by W.Langdon from gp-bibliography.bib Revision:1.4067

@InProceedings{McPhee:2017:GECCO,
  author =       "Nicholas Freitag McPhee and Maggie M. Casale and 
                 Mitchell Finzel and Thomas Helmuth and Lee Spector",
  title =        "Visualizing Genetic Programming Ancestries Using Graph
                 Databases",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference Companion",
  series =       "GECCO '17",
  year =         "2017",
  isbn13 =       "978-1-4503-4939-0",
  address =      "Berlin, Germany",
  pages =        "245--246",
  size =         "2 pages",
  URL =          "http://doi.acm.org/10.1145/3067695.3075617",
  DOI =          "doi:10.1145/3067695.3075617",
  acmid =        "3075617",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  keywords =     "genetic algorithms, genetic programming, ancestry,
                 graph database, visualization",
  month =        "15-19 " # jul,
  abstract =     "Previous work has demonstrated the utility of graph
                 databases as a tool for collecting and analysing
                 ancestry in evolutionary computation runs. That work
                 focused on sections of individual runs, whereas this
                 poster illustrates the application of these ideas on
                 the entirety of large runs (up to one million
                 individuals) and combinations of multiple runs. Here we
                 use these tools to generate graphs showing all the
                 ancestors of successful individuals from a variety of
                 stack-based genetic programming runs on software
                 synthesis problems. These graphs highlight important
                 moments in the evolutionary process. They also allow us
                 to compare the dynamics when using different
                 evolutionary tools, such as different selection
                 mechanisms or representations, as well as comparing the
                 dynamics for successful and unsuccessful runs.",
  notes =        "Also known as \cite{McPhee:2017:VGP:3067695.3075617}
                 GECCO-2017 A Recombination of the 26th International
                 Conference on Genetic Algorithms (ICGA-2017) and the
                 22nd Annual Genetic Programming Conference (GP-2017)",
}

Genetic Programming entries for Nicholas Freitag McPhee Maggie M Casale Mitchell Finzel Thomas Helmuth Lee Spector

Citations