Mutation, Sexual Reproduction and Survival in Dynamic Environments

Authors Ruta Mehta, Ioannis Panageas, Georgios Piliouras, Prasad Tetali, Vijay V. Vazirani



PDF
Thumbnail PDF

File

LIPIcs.ITCS.2017.16.pdf
  • Filesize: 0.69 MB
  • 29 pages

Document Identifiers

Author Details

Ruta Mehta
Ioannis Panageas
Georgios Piliouras
Prasad Tetali
Vijay V. Vazirani

Cite As Get BibTex

Ruta Mehta, Ioannis Panageas, Georgios Piliouras, Prasad Tetali, and Vijay V. Vazirani. Mutation, Sexual Reproduction and Survival in Dynamic Environments. In 8th Innovations in Theoretical Computer Science Conference (ITCS 2017). Leibniz International Proceedings in Informatics (LIPIcs), Volume 67, pp. 16:1-16:29, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017) https://doi.org/10.4230/LIPIcs.ITCS.2017.16

Abstract

A new approach to understanding evolution [Valiant, JACM 2009], namely viewing it through the lens of computation,
has already started yielding new insights, e.g., natural selection under sexual reproduction can be interpreted
as the Multiplicative Weight Update (MWU) Algorithm in coordination games played among genes [Chastain, Livnat, Papadimitriou, Vazirani, PNAS 2014]. Using this machinery, we study the role of mutation in changing environments in the presence of sexual reproduction. Following [Wolf, Vazirani, Arkin, J. Theor. Biology], we model changing environments via a Markov chain, with the states representing environments, each with its own fitness matrix. In this setting, we show that in the absence of mutation, the population goes extinct, but in the presence of mutation, the population survives with positive probability.

On the way to proving the above theorem, we need to establish some facts about dynamics in games. We provide the first, to our knowledge, polynomial convergence bound for noisy MWU in a coordination game. 
Finally, we also show that in static environments, sexual evolution with mutation converges, for any level of mutation.

Subject Classification

Keywords
  • Evolution
  • Non-linear dynamics
  • Mutation

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads

References

  1. H. Ackermann, P. Berenbrink, S. Fischer, and M. Hoefer. Concurrent imitation dynamics in congestion games. Proceedings of the 28th ACM symposium on Principles of distributed computing (PODC), pages 63-72, 2009. Google Scholar
  2. S. Arora, M. Hardt, and N. Vishnoi. Off the convex path, 2015. URL: http://www.offconvex.org.
  3. S. Arora, E. Hazan, and S. Kale. The multiplicative weights update method: a meta algorithm and applications. Technical report, Princeton, 2005. Google Scholar
  4. L. Baum and J. Eagon. An inequality with applications to statistical estimation for probabilistic functions of markov processes and to a model for ecology. Bull. Amer. Math. Soc., 73:360-363, 1967. Google Scholar
  5. J. Branke and W. Wang. Genetic and Evolutionary Computation - GECCO 2003: Genetic and Evolutionary Computation Conference Chicago, IL, USA, July 12-16, 2003 Proceedings, Part I, chapter Theoretical Analysis of Simple Evolution Strategies in Quickly Changing Environments, pages 537-548. Springer Berlin Heidelberg, Berlin, Heidelberg, 2003. Google Scholar
  6. M. Braverman, A. Grigo, and C. Rojas. Noise vs computational intractability in dynamics. Innovations in Theoretical Computer Science Conference (ITCS), pages 128-141, 2012. Google Scholar
  7. E. Chastain, A. Livnat, C. H. Papadimitriou, and U. V. Vazirani. Multiplicative updates in coordination games and the theory of evolution. Innovations in Theoretical Computer Science Conference (ITCS), pages 57-58, 2013. Google Scholar
  8. E. Chastain, A. Livnat, C.H. Papadimitriou, and U. Vazirani. Algorithms, games, and evolution. Proceedings of the National Academy of Sciences (PNAS), 2014. Google Scholar
  9. S. Chien and A. Sinclair. Convergence to approximate nash equilibria in congestion games. Games and Economic Behavior, pages 169-178, 2007. Google Scholar
  10. Devdatt P. Dubhashi and Alessandro Panconesi. Concentration of Measure for the Analysis of Randomized Algorithms. Cambridge University Press, 2009. Google Scholar
  11. Willliam Feller. An introduction to probability theory and its applications, volume 2. John Wiley &Sons, 2008. Google Scholar
  12. R. Ge, F. Huang, C. Jin, and Y. Yuan. Escaping from saddle points - online stochastic gradient for tensor decomposition. Conference on Learning Theory (COLT), 2015. Google Scholar
  13. J. Hofbauer and K. Sigmund. Evolutionary Games and Population Dynamics. Cambridge University Press, Cambridge, 1998. Google Scholar
  14. R. Kleinberg, G. Piliouras, and É. Tardos. Multiplicative updates outperform generic no-regret learning in congestion games. ACM Symposium on Theory of Computing (STOC), 2009. Google Scholar
  15. J. D. Lee, M. Simchowitz, M. I Jordan, and B. Recht. Gradient descent converges to minimizers. Conference on Learning Theory (COLT), 2016. Google Scholar
  16. A. ML Liekens. Evolution of finite populations in dynamic environments. Technische Universiteit Eindhoven, 2005. Google Scholar
  17. A. Livnat, C. H. Papadimitriou, J. Dushoff, and M. W. Feldman. A mixability theory for the role of sex in evolution. Proceedings of the National Academy of Sciences (PNAS), 105(50):19803-19808, 2008. Google Scholar
  18. A. Livnat, C.H. Papadimitriou, A. Rubinstein, A. Wan, and G. Valiant. Satisfiability and evolution. IEEE Symposium on. Foundations of Computer Science (FOCS), 2014. Google Scholar
  19. V. Losert and E. Akin. Dynamics of games and genes: Discrete versus continuous time. Journal of Mathematical Biology, 1983. Google Scholar
  20. Y. Lyubich. Mathematical Structures in Population Genetics. Springer-Verlag, 1992. Google Scholar
  21. Y. Lyubich, G.Maistrovski, and Yu.Ol'khovski. Problems of information transmission, 1980. Google Scholar
  22. R. Mehta, I. Panageas, and G. Piliouras. Natural selection as an inhibitor of genetic diversity: Multiplicative weights updates algorithm and a conjecture of haploid genetics. Innovations in Theoretical Computer Science (ITCS), 2015. Google Scholar
  23. R. Mehta, I. Panageas, G. Piliouras, and S. Yazdanbod. The Computational Complexity of Genetic Diversity. European Symposium on Algorithms (ESA), 2016. URL: http://arxiv.org/abs/1411.6322.
  24. R. Meir and D. Parkes. A note on sex, evolution, and the multiplicative updates algorithm. Proceedings of the 12th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2015. Google Scholar
  25. James Meiss. Differential Dynamical Systems. SIAM, 2007. Google Scholar
  26. T Nagylaki. The evolution of multilocus systems under weak selection. Genetics, 134(2):627-47, 1993. Google Scholar
  27. M. A. Nowak, N. L. Komarova, and P. Niyogi. Evolution of universal grammar. Science, 2001. Google Scholar
  28. I. Panageas and G. Piliouras. Average Case Performance of Replicator Dynamics in Potential Games via Computing Regions of Attraction. ACM Conference on Economics and Computation (EC), 2016. Google Scholar
  29. I. Panageas and G. Piliouras. Gradient descent only converges to minimizers: Non-isolated critical points and invariant regions. Innovations in Theoretical Computer Science (ITCS), 2017. Google Scholar
  30. I. Panageas, P. Srivastava, and N. K. Vishnoi. Evolutionary dynamics in finite populations mix rapidly. ACM-SIAM Symposium on Discrete Algorithms (SODA), pages 480-497, 2016. Google Scholar
  31. I. Panageas and N. K. Vishnoi. Mixing time of markov chains, dynamical systems and evolution. International Colloquium on Automata, Languages and Programming (ICALP), 2016. Google Scholar
  32. R. Pemantle. Nonconvergence to unstable points in urn models and stochastic approximations. The Annals of Probability, 18(2), 1990. Google Scholar
  33. O. Rivoire and S. Leibler2. The value of information for populations in varying environments. ArXiv e-prints, 2010. Google Scholar
  34. L. G. Valiant. Evolvability. J. ACM, 56(1), 2009. Google Scholar
  35. D. M. Wolf, V. V. Vazirani, and A. P. Arkin. Diversity in times of adversity: probabilistic strategies in microbial survival games. Journal of theoretical biology, 234(2):227-253, 2005. Google Scholar
  36. S. Yang, Y. Ong, and Y. Jin. Evolutionary computation in dynamic and uncertain environments, volume 51. Springer Science &Business Media, 2007. Google Scholar
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail