Nicholas Freitag McPhee
Lee Spector
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.
- Thomas Helmuth. 2015. General Program Synthesis from Examples Using Genetic Programming with Parent Selection Based on Random Lexicographic Orderings of Test Cases. Ph.D. dissertation. University of Massachusetts, Amherst. http://scholarworks.umass.edu/dissertations_2/465/Google Scholar
- Thomas Helmuth and Lee Spector. 2015. General program synthesis benchmark suite. In GECCO '15: Proceedings of the 2015 Conference on Genetic and Evolutionary Computation (July, 2015). Google ScholarDigital Library
- Nicholas Freitag McPhee, David Donatucci, and Thomas Helmuth. 2016. Using Graph Databases to Explore the Dynamics of Genetic Programming Runs. In Genetic Programming Theory and Practice XIII, R. Riolo, B. Worzel, M. Kotanchek, and A. Kordon (Eds.). Springer.Google Scholar
Index Terms
- Visualizing genetic programming ancestries using graph databases
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