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What Else Is in an Evolved Name? Exploring Evolvable Specificity with SignalGP

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Book cover Genetic Programming Theory and Practice XVI

Part of the book series: Genetic and Evolutionary Computation ((GEVO))

Abstract

Tags are evolvable labels that provide genetic programs a flexible mechanism for specification. Tags are used to label and refer to programmatic elements, such as functions or jump targets. However, tags differ from traditional, more rigid methods for handling labeling because they allow for inexact references; that is, a referring tag need not exactly match its referent. Here, we explore how adjusting the threshold for how what qualifies as a match affects adaptive evolution. Further, we propose broadened applications of tags in the context of a genetic programming (GP) technique called SignalGP. SignalGP gives evolution direct access to the event-driven paradigm. Program modules in SignalGP are tagged and can be triggered by signals (with matching tags) from the environment, from other agents, or due to internal regulation. Specifically, we propose to extend this tag based system to: (1) provide more fine-grained control over module execution and regulation (e.g., promotion and repression) akin to natural gene regulatory networks, (2) employ a mosaic of GP representations within a single program, and (3) facilitate major evolutionary transitions in individuality (i.e., allow hierarchical program organization to evolve de novo).

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References

  1. Banzhaf, W.: Artificial Regulatory Networks and Genetic Programming. In: Genetic Programming Theory and Practice, pp. 43–61. Springer US, Boston, MA (2003)

    Chapter  Google Scholar 

  2. Cassandras, C.G.: The event-driven paradigm for control, communication and optimization. Journal of Control and Decision 1, 3–17 (2014)

    Article  Google Scholar 

  3. Dolson, E., Lalejini, A., Ofria, C.: Exploring genetic programming systems with MAP-Elites. In: Banzhaf, W., Spector, L., Sheneman, L. (eds.) Genetic Programming Theory and Practice XVI. Springer International Publishing, Cham (2018). https://doi.org/10.1007/978-3-030-04735-1_1

    Google Scholar 

  4. Etzion, O., Niblett, P.: Event Processing in Action. ISBN: 9781935182214. Manning Publications (2010)

    Google Scholar 

  5. Heemels, W.P., Johansson, K.H., Tabuada, P.: An introduction to event-triggered and self-triggered control. Proceedings of the IEEE Conference on Decision and Control pp. 3270–3285 (2012)

    Google Scholar 

  6. Hintze, A., Edlund, J.A., Olson, R.S., Knoester, D.B., Schossau, J., Albantakis, L., Tehrani-Saleh, A., Kvam, P., Sheneman, L., Goldsby, H., Bohm, C., Adami, C.: Markov Brains: A Technical Introduction. arXiv 1709.05601 (2017)

    Google Scholar 

  7. Hintze, A., Schossau, J., Bohm, C.: The evolutionary Buffet method. In: Banzhaf, W., Spector, L., Sheneman, L. (eds.) Genetic Programming Theory and Practice XVI. Springer International Publishing, Cham (2018). http://10.1007/978-3-030-04735-1_2

    Google Scholar 

  8. Holland, J.: The effect of labels (tags) on social interactions. Santa Fe Inst., Santa Fe, NM, Working Paper pp. 93–10 (1993). URL http://samoa.santafe.edu/media/workingpapers/93-10-064.pdf

  9. Holland, J.H.: Genetic Algorithms and Classifier Systems: Foundations and Future Directions. In: Proceedings of the 2nd International Conference on Genetic Algorithms (ICGA87), pp. 82–89 (1987)

    Google Scholar 

  10. Holland, J.H.: Concerning the emergence of tag-mediated lookahead in classifier systems. Physica D: Nonlinear Phenomena 42, 188–201 (1990). DOI 10.1016/0167-2789(90)90073-X

    Article  Google Scholar 

  11. Holland, J.H.: Studying complex adaptive systems. Journal of Systems Science and Complexity 19, 1–8 (2006)

    Article  MathSciNet  Google Scholar 

  12. Kelly, S., Heywood, M.I.: Emergent Tangled Graph Representations for Atari Game Playing Agents. In: European Conference on Genetic Programming (EuroGP-2017), pp. 64–79 Springer (2017)

    Google Scholar 

  13. Lalejini, A.: amlalejini/GPTP-2018-Exploring-Evolvable- Specificity-with-SignalGP (2018). Available on GitHub at https://github.com/amlalejini/GPTP-2018-Exploring-Evolvable-Specificity-with-SignalGP

  14. Lalejini, A., Ofria, C.: Evolving Event-driven Programs with SignalGP. arXiv 1804.05445 (2018)

    Google Scholar 

  15. Lalejini, A., Wiser, M.J., Ofria, C.: Gene Duplications Drive the Evolution of Complex Traits and Regulation. Proceedings of the European Conference on Artificial Life (ALIFE-2017), 4–8 (2017)

    Google Scholar 

  16. Lopes, R.L., Costa, E.: Genetic programming with genetic regulatory networks. In: Proceeding of the Fifteenth Annual Conference on Genetic and Evolutionary Computation - GECCO ’13, p. 965. ACM Press, New York, New York, USA (2013)

    Google Scholar 

  17. Mouret, J.B., Clune, J.: Illuminating search spaces by mapping elites. arXiv 1504.04909, 1–15 (2015)

    Google Scholar 

  18. R Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2016). URL https://www.R-project.org/

  19. Smith, J.M., Szathmary, E.: The major transitions in evolution. Oxford University Press (1997)

    Google Scholar 

  20. Spector, L., Harringtion, K., Martin, B., Helmuth, T.: What’s in an Evolved Name? The Evolution of Modularity via Tag-Based Reference. In: R. Riolo, E. Vladislavleva, J.H. Moore (eds.) Genetic Programming Theory and Practice IX, Genetic and Evolutionary Computation, chap. 1, pp. 1–16. Springer New York, New York, NY (2011)

    Google Scholar 

  21. Spector, L., Harrington, K., Helmuth, T.: Tag-based modularity in tree-based genetic programming. GECCO ’12: Proceedings of the Fourteenth International Conference on Genetic and Evolutionary Computation pp. 815–822 (2012)

    Google Scholar 

  22. Spector, L., Martin, B., Harrington, K., Helmuth, T.: Tag-based modules in genetic programming. GECCO ’11: Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation pp. 1419–1426 (2011)

    Google Scholar 

  23. Waskom, M., et al. : mwaskom/seaborn: v0.8.1 (september 2017) (2017). URL https://doi.org/10.5281/zenodo.883859

  24. West, S.A., Fisher, R.M., Gardner, A., Kiers, E.T.: Major evolutionary transitions in individuality. Proceedings of the National Academy of Sciences 112, 10,112–10,119 (2015)

    Article  Google Scholar 

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Acknowledgements

We extend our thanks to the members of the Digital Evolution Laboratory at Michigan State University and the attendees of the 2018 Genetic Programming Theory and Practice Workshop for thoughtful discussions and feedback on this work. This research was supported by the National Science Foundation (NSF) through the BEACON center (Cooperative Agreement DBI-0939454), a Graduate Research Fellowship to AL (Grant No. DGE-1424871), and NSF Grant No. DEB-1655715 to CO. Michigan State University provided computational resources through the Institute for Cyber-Enabled Research. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF or MSU.

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Authors and Affiliations

  1. BEACON Center for the Study of Evolution in Action and Department of Computer Science and Ecology, Evolutionary Biology, and Behavior Program, Michigan State University, East Lansing, MI, USA

    Alexander Lalejini & Charles Ofria

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  1. Alexander Lalejini
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  2. Charles Ofria
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Correspondence to Alexander Lalejini .

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Editors and Affiliations

  1. Computer Science and Engineering, John R. Koza Chair, Michigan State University, East Lansing, MI, USA

    Wolfgang Banzhaf

  2. Cognitive Science, Hampshire College, Amherst, MA, USA

    Lee Spector

  3. Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA

    Leigh Sheneman

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Lalejini, A., Ofria, C. (2019). What Else Is in an Evolved Name? Exploring Evolvable Specificity with SignalGP. In: Banzhaf, W., Spector, L., Sheneman, L. (eds) Genetic Programming Theory and Practice XVI. Genetic and Evolutionary Computation. Springer, Cham. https://doi.org/10.1007/978-3-030-04735-1_6

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  • DOI: https://doi.org/10.1007/978-3-030-04735-1_6

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  • Print ISBN: 978-3-030-04734-4

  • Online ISBN: 978-3-030-04735-1

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