Visualizing Genetic Programming Ancestries

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{McPhee:2016:GECCOcomp,
  author =       "Nicholas Freitag McPhee and Maggie M. Casale and 
                 Mitchell Finzel and Thomas Helmuth and Lee Spector",
  title =        "Visualizing Genetic Programming Ancestries",
  booktitle =    "GECCO '16 Companion: Proceedings of the Companion
                 Publication of the 2016 Annual Conference on Genetic
                 and Evolutionary Computation",
  year =         "2016",
  isbn13 =       "978-1-4503-4323-7",
  pages =        "1419--1426",
  keywords =     "genetic algorithms, genetic programming, pushGP, liner
                 genetic programming, Clojush, lexicase selection",
  month =        "20-24 " # jul,
  organisation = "SIGEVO",
  address =      "Denver, Colorado, USA",
  DOI =          "doi:10.1145/2908961.2931741",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  size =         "8 pages",
  abstract =     "Previous work has demonstrated the utility of graph
                 databases as a tool for collecting, analysing, and
                 visualizing ancestry in evolutionary computation runs.
                 That work focused on sections of individual runs,
                 whereas this paper illustrates the application of these
                 ideas on the entirety of large runs (up to three
                 hundred thousand 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 for
                 successful and unsuccessful runs. As well as displaying
                 these full ancestry graphs, we use a variety of
                 standard techniques such as size, colour, pattern,
                 labelling, and opacity to visualize other important
                 information such as fitness, which genetic operators
                 were used, and the distance between parent and child
                 genomes. While this generates an extremely rich
                 visualization, the amount of data can also be somewhat
                 overwhelming, so we also explore techniques for
                 filtering these graphs that allow us to better
                 understand the key dynamics.",
  notes =        "Titan graph database. Tinkerpop query tools. Graphviz
                 dot. Mutation and crossover. Hyperselection events.
                 Replace space with newline benchmark. p1420 'uses
                 restricted Boltzmann machines (RBMs) to compress the
                 200 error values into 24-bit RGB color values' p1423
                 'presence of an individual could have had..impact..even
                 if..never contributed genetic material' p1423
                 'unfilter' p1426 future dynamic tools.

                 My pdf reader barfs

                 Slides
                 https://www.slideshare.net/NicMcPhee/visualizing-genetic-programming-ancestries
                 Cites \cite{series/sci/BurlacuAWKK15}",
}

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

Citations