Self-referencing cellular automata: A model of the evolution of information control in biological systems

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

Cellular automata have been useful artificial models for exploring how relatively simple rules combined with spatial memory can give rise to complex emergent patterns. Moreover, studying the dynamics of how rules emerge under artificial selection for function has recently become a powerful tool for understanding how evolution can innovate within its genetic rule space. However, conventional cellular automata lack the kind of state feedback that is surely present in natural evolving systems. Each new generation of a population leaves an indelible mark on its environment and thus affects the selective pressures that shape future generations of that population. To model this phenomenon, we have augmented traditional cellular automata with state-dependent feedback. Rather than generating automata executions from an initial condition and a static rule, we introducemappings which generate iteration rules from the cellular automaton itself. We show that these new automata contain disconnected regions which locally act like conventional automata, thus encapsulating multiple functions into one structure. Consequently, we have provided a new model for processes like cell differentiation. Finally, by studying the size of these regions, we provide additional evidence that the dynamics of self-reference may be critical to understanding the evolution of natural language. In particular, the rules of elementary cellular automata appear to be distributed in the same way as words in the corpus of a natural language.

Original languageEnglish (US)
Title of host publicationArtificial Life 14 - Proceedings of the 14th International Conference on the Synthesis and Simulation of Living Systems, ALIFE 2014
EditorsHiroki Sayama, John Rieffel, Sebastian Risi, Rene Doursat, Hod Lipson
PublisherMIT Press Journals
Pages522-529
Number of pages8
ISBN (Electronic)9780262326216
StatePublished - Jan 1 2014
Event14th International Conference on the Synthesis and Simulation of Living Systems, ALIFE 2014 - Manhattan, United States
Duration: Jul 30 2014Aug 2 2014

Publication series

NameArtificial Life 14 - Proceedings of the 14th International Conference on the Synthesis and Simulation of Living Systems, ALIFE 2014

Conference

Conference14th International Conference on the Synthesis and Simulation of Living Systems, ALIFE 2014
CountryUnited States
CityManhattan
Period7/30/148/2/14

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Artificial Intelligence
  • Modeling and Simulation

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    Pavlic, T. P., Adams, A. M., Davies, P. C. W., & Walker, S. I. (2014). Self-referencing cellular automata: A model of the evolution of information control in biological systems. In H. Sayama, J. Rieffel, S. Risi, R. Doursat, & H. Lipson (Eds.), Artificial Life 14 - Proceedings of the 14th International Conference on the Synthesis and Simulation of Living Systems, ALIFE 2014 (pp. 522-529). (Artificial Life 14 - Proceedings of the 14th International Conference on the Synthesis and Simulation of Living Systems, ALIFE 2014). MIT Press Journals.