Visualizing genetic programming ancestries using graph databases

被引：3

作者：

McPhee, Nicholas Freitag ^{[1
]}

Casale, Maggie M. ^{[2
]}

Finzel, Mitchell ^{[1
]}

Helmuth, Thomas ^{[3
]}

Spector, Lee ^{[4
]}

机构：

[1] Univ Minnesota, Morris, MN 56267 USA

[2] Design Ctr Inc, St Paul, MN USA

[3] Washington & Lee Univ, Lexington, VA 24450 USA

[4] Hampshire Coll, Amherst, MA 01002 USA

来源：

PROCEEDINGS OF THE 2017 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE COMPANION (GECCO'17 COMPANION) | 2017年

基金：

美国国家科学基金会;

关键词：

visualization; genetic programming; graph database; ancestry;

D O I：

10.1145/3067695.3075617

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：245 / 246

页数：2

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