Tag-based Modularity in Tree-based Genetic Programming

This page contains material related to "Tag-based Modularity in Tree-based Genetic Programming," a paper in the Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2012), published by The Association of Computing Machinery.

Abstract

Several techniques have been developed for allowing genetic programming systems to produce programs that make use of subroutines, macros, and other modular program structures. A recently proposed technique, based on the ``tagging'' and tag-based retrieval of blocks of code, has been shown to have novel and desirable features but this was demonstrated only within the context of the PushGP genetic programming system. Following a suggestion in the GECCO-2010 publication on this technique we show here how tag-based modules can be incorporated into a more standard tree-based genetic programming system. We describe the technique in detail along with some possible extensions, outline arguments for its simplicity and potential power, and present results obtained using the technique on problems for which other modularization techniques have been shown to be useful. The results are mixed; substantial benefits are seen on the lawnmower problem but not on the Boolean even-4-parity problem. We discuss the observed results and directions for future research.

Citation

Spector, L., K. Harrington, and T. Helmuth. 2012. Tag-based Modularity in Tree-based Genetic Programming. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2012). ACM Press. (In press).

Full paper

Full paper in PDF format: [coming soon]

Presentation Slides

[coming soon]

Source Code

taggp_final.zip: This is the source code that produced the results in the paper. It is written in version 1.3 of Clojure, which is a dialect of Lisp for the JVM.

Acknowledgments

Thanks to Emma Tosch, Omri Bernstein, and Kwaku Yeboah Antwi for discussions related to this work and comments on a draft, to Josiah Erikson for systems support, and to Hampshire College for support for the Hampshire College Institute for Computational Intelligence. Computational support was partially provided by the Brandeis HPC. This material is based upon work supported by the National Science Foundation under Grant No. 1017817. Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of the National Science Foundation.