Visualizing genetic programming ancestries

Nicholas Freitag McPhee, Maggie M. Casale, Mitchell Finzel, Thomas Helmuth, Lee Spector

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Scopus citations

Abstract

Previous work has demonstrated the utility of graph databases as a tool for collecting, analyzing, 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, color, pattern, labeling, 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.

Original languageEnglish (US)
Title of host publicationGECCO 2016 Companion - Proceedings of the 2016 Genetic and Evolutionary Computation Conference
EditorsTobias Friedrich
PublisherAssociation for Computing Machinery, Inc
Pages1419-1426
Number of pages8
ISBN (Electronic)9781450343237
DOIs
StatePublished - Jul 20 2016
Event2016 Genetic and Evolutionary Computation Conference, GECCO 2016 Companion - Denver, United States
Duration: Jul 20 2016Jul 24 2016

Publication series

NameGECCO 2016 Companion - Proceedings of the 2016 Genetic and Evolutionary Computation Conference

Other

Other2016 Genetic and Evolutionary Computation Conference, GECCO 2016 Companion
Country/TerritoryUnited States
CityDenver
Period7/20/167/24/16

Bibliographical note

Funding Information:
This material is based upon work supported by the National Science Foundation under Grants No. 1129139 and 1331283.

Publisher Copyright:
© 2016 ACM.

Keywords

  • Ancestry
  • Evolutionary computation
  • Genetic programming
  • Lineage
  • Visualization

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