Abstract
Previous work has demonstrated the utility of graph databases as a tool for collecting and analyzing 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.
| Original language | English (US) |
|---|---|
| Title of host publication | GECCO 2017 - Proceedings of the 2017 Genetic and Evolutionary Computation Conference |
| Publisher | Association for Computing Machinery |
| Pages | 245-246 |
| Number of pages | 2 |
| ISBN (Electronic) | 9781450349208 |
| DOIs | |
| State | Published - Jul 1 2017 |
| Event | 2017 Genetic and Evolutionary Computation Conference, GECCO 2017 - Berlin, Germany Duration: Jul 15 2017 → Jul 19 2017 |
Publication series
| Name | GECCO 2017 - Proceedings of the 2017 Genetic and Evolutionary Computation Conference |
|---|
Conference
| Conference | 2017 Genetic and Evolutionary Computation Conference, GECCO 2017 |
|---|---|
| Country/Territory | Germany |
| City | Berlin |
| Period | 7/15/17 → 7/19/17 |
Bibliographical note
Publisher Copyright:© 2017 Owner/Author.
Keywords
- ancestry
- genetic programming
- graph database
- visualization
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