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

  • Alexander LalejiniEmail author
  • Charles Ofria
Chapter
Part of the Genetic and Evolutionary Computation book series (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).

Notes

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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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.BEACON Center for the Study of Evolution in Action and Department of Computer Science and Ecology, Evolutionary Biology, and Behavior ProgramMichigan State UniversityEast LansingUSA

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