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Neutrality, Robustness, and Evolvability in Genetic Programming

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Genetic Programming Theory and Practice XIV

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

Abstract

Redundant mapping from genotype to phenotype is common in evolutionary algorithms, especially in genetic programming (GP). Such a redundancy leads to neutrality, a situation where mutations to a genotype may not alter its phenotypic outcome. The effects of neutrality can be better understood by quantitatively analyzing its two observed properties, robustness and evolvability. In this chapter, we summarize our previous work on this topic in examining a compact Linear GP algorithm. Due to the choice of this particular system we can characterize its entire genotype, phenotype, and fitness networks, and quantitatively measure robustness and evolvability at the genotypic, phenotypic, and fitness levels. We then investigate the relationship between robustness and evolvability at those different levels. Technically, we use an ensemble of random walkers and hill climbers to study how robustness and evolvability are related to the structure of genotypic, phenotypic, and fitness networks and influence the evolutionary search process.

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References

  1. Banzhaf, W.: Genotype-phenotype mapping and neutral variation - a case study in genetic programming. In: Davidor, Y., Schwefel, H.P., Manner, R. (eds.) Parallel Problem Solving from Nature. Lecture Notes in Computer Science, vol. 866, pp. 322–332. Springer, Berlin (1994)

    Google Scholar 

  2. Banzhaf, W., Leier, A.: Evolution on neutral networks in genetic programming. In: Yu, T., Riolo, R., Worzel, B. (eds.) Genetic Programming Theory and Practice III, chap. 14, pp. 207–221. Springer, Berlin (2006)

    Chapter  Google Scholar 

  3. Banzhaf, W., Beslon, G., Christensen, S., Foster, J.A., Kepes, F., Lefort, V., Miller, J.F., Radman, M., Ramsden, J.J.: From artificial evolution to computational evolution: a research agenda. Nat. Rev. Genet. 7, 729–735 (2006)

    Article  Google Scholar 

  4. Brameier, M.F., Banzhaf, W.: A comparison of linear genetic programming and neural networks in medical data mining. IEEE Trans. Evol. Comput. 5(1), 17–26 (2001)

    Article  Google Scholar 

  5. Brameier, M.F., Banzhaf, W.: Linear Genetic Programming. Springer, Berlin (2007)

    MATH  Google Scholar 

  6. Ciliberti, S., Martin, O.C., Wagner, A.: Innovation and robustness in complex regulatory gene networks. Proc. Natl. Acad. Sci. 104(34), 13591–13596 (2007)

    Article  Google Scholar 

  7. Collins, M.: Finding needles in haystacks is harder with neutrality. Genet. Program. Evolvable Mach. 7, 131–144 (2006)

    Article  Google Scholar 

  8. Cowperthwaite, M.C., Economo, E.P., Harcombe, W.R., Miller, E.L., Meyers, L.A.: The ascent of the abundant: how mutational networks constrain evolution. PLoS Computat. Biol. 4(7), e1000110 (2008)

    Article  MathSciNet  Google Scholar 

  9. Draghi, J.A., Parsons, T.L., Wagner, G.P., Plotkin, J.B.: Mutational robustness can facilitate adaptation. Nature 463, 353–355 (2010)

    Article  Google Scholar 

  10. Ferrada, E., Wagner, A.: Protein robustness promotes evolutionary innovations on large evolutionary time-scales. Proc. R. Soc. B 275, 1595–1602 (2008)

    Article  Google Scholar 

  11. Fontana, W., Schuster, P.: Continuity in evolution: on the nature of transitions. Science 280, 1451–1455 (1998)

    Article  Google Scholar 

  12. Galvan-Lopez, E., Poli, R.: An empirical investigation of how and why neutrality affects evolutionary search. In: Cattolico, M. (ed.) Proceedings of the Genetic and Evolutionary Computation Conference, pp. 1149–1156. Springer, Berlin (2006)

    Google Scholar 

  13. Guven, A.: Linear genetic programming for time-series modeling of daily flow rate. J. Earth Syst. Sci. 118(2), 137–146 (2009)

    Article  Google Scholar 

  14. Hu, T., Banzhaf, W.: Neutrality and variability: two sides of evolvability in linear genetic programming. In: Rothlauf, F. (ed.) Proceedings of the Genetic and Evolutionary Computation Conference, pp 963–970 (2009)

    Google Scholar 

  15. Hu, T., Banzhaf, W.: Evolvability and speed of evolutionary algorithms in light of recent developments in biology. J. Artif. Evol. Appl. 2010, 568375 (2010)

    Google Scholar 

  16. Hu, T., Payne, J.L., Banzhaf, W., Moore, J.H.: Robustness, evolvability, and accessibility in linear genetic programming. In: Silva, S., Foster, J.A., Nicolau, M., Machado, P., Giacobini, M. (eds.) Proceedings of the European Conference on Genetic Programming. Lecture Notes in Computer Science, vol. 6621, pp. 13–24. Springer, Berlin (2011)

    Google Scholar 

  17. Hu, T., Payne, J.L., Banzhaf, W., Moore, J.H.: Evolutionary dynamics on multiple scales: A quantitative analysis of the interplay between genotype, phenotype, and fitness in linear genetic programming. Genet. Program. Evolvable Mach. 13, 305–337 (2012)

    Article  Google Scholar 

  18. Hu, T., Moore, J.H., Banzhaf, W.: The effects of recombination on phenotypic exploration and robustness in evolution. Artif. Life 20(4), 457–470 (2014)

    Article  Google Scholar 

  19. Landry, C.R., Lemos, B., Rifkin, S.A., Dickinson, W.J., Hartl, D.L.: Genetic properties influcing the evolvability of gene expression. Science 317, 118–121 (2007)

    Article  Google Scholar 

  20. Masel, J., Trotter, M.V.: Robustness and evolvability. Trends Genet. 26, 406–414 (2010)

    Article  Google Scholar 

  21. McBride, R.C., Ogbunugafor, C.B., Turner, P.E.: Robustness promotes evolvability of thermotolerance in an RNA virus. BMC Evol. Biol. 8, 231 (2008)

    Article  Google Scholar 

  22. Reidys, C., Stadler, P.F., Schuster, P.: Generic properties of combinatory maps: neutral networks of RNA secondary structures. Bull. Math. Biol. 59(2), 339–397 (1997)

    Article  Google Scholar 

  23. Reisinger, J., Miikkulainen, R.: Acquring evolvability through adaptive representation. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 1045–1052 (2007)

    Google Scholar 

  24. Rodrigues, J., Wagner, A.: Genotype networks, innovation, and robustness in sulfur metabolism. BMC Syst. Biol. 5, 39 (2011)

    Article  Google Scholar 

  25. Rothlauf, F., Goldberg, D.E.: Redundant representations in evolutionary computation. Evol. Comput. 11(4), 381–415 (2003)

    Article  Google Scholar 

  26. Schuster, P., Fontana, W., Stadler, P.F., Hofacker, I.L.: From sequences to shapes and back: a case study in RNA secondary structures. Proc. R. Soc. B 255, 279–284 (1994)

    Article  Google Scholar 

  27. Smith, T., Husbands, P., O’Shea, M.: Neutral networks and evolvability with complex genotype-phenotype mapping. In: Kelemen, J., Sosik, P. (eds.) Proceedings of the European Conference on Artificial Life, Lecture Notes in Artificial Intelligence, vol. 2159, pp. 272–281. Springer, Berlin (2001)

    Google Scholar 

  28. Song, D., Heywood, M.I., Zincir-Heywod, A.: A linear genetic programming approch to intrusion detection. In: Proceedings of the Genetic and Evolutionary Computation Conference. Lecture Notes in Computer Science, vol. 2724. Springer, Berlin (2001)

    Google Scholar 

  29. Soule, T.: Resilient individuals improve evolutionary search. Artif. Life 12, 17–34 (2006)

    Article  Google Scholar 

  30. Wagner, A.: Robustness, evolvability, and neutrality. Fed. Eur. Biochem. Soc. Lett. 579(8), 1772–1778 (2005)

    Article  Google Scholar 

  31. Wagner, A.: Robustness and evolvability: a paradox resolved. Proc. R. Soc. B 275(1630), 91–100 (2008)

    Article  Google Scholar 

  32. Yu, T., Miller, J.F.: Through the interaction of neutral and adaptive mutations, evolutionary search finds a way. Artif. Life 12, 525–551 (2006)

    Article  Google Scholar 

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Acknowledgements

TH is supported by the Ignite R&D fund of Research and Development Corporation of Newfoundland and Labrador and the Canadian Natural Sciences and Engineering Research Council (NSERC) Discovery grant RGPIN-04699-2016. WB acknowledges the support from the Canadian Natural Sciences and Engineering Research Council (NSERC) Discovery grant RGPIN-283304-2012.

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Authors and Affiliations

  1. Department of Computer Science, Memorial University, St. John’s, NL, Canada

    Ting Hu & Wolfgang Banzhaf

  2. BEACON Center of the Study of Evolution in Action, Michigan State University, East Lansing, MI, USA

    Wolfgang Banzhaf

Authors
  1. Ting Hu
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  2. Wolfgang Banzhaf
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Correspondence to Ting Hu .

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Editors and Affiliations

  1. Center for the Study of Complex Sys, University of Michigan, Ann Arbor, MI, USA

    Rick Riolo

  2. Evolution Enterprises, Ann Arbor, MI, USA

    Bill Worzel

  3. Colorado State University, Fort Collins, CO, USA

    Brian Goldman

  4. Ann Arbor, MI, USA

    Bill Tozier

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Hu, T., Banzhaf, W. (2018). Neutrality, Robustness, and Evolvability in Genetic Programming. In: Riolo, R., Worzel, B., Goldman, B., Tozier, B. (eds) Genetic Programming Theory and Practice XIV. Genetic and Evolutionary Computation. Springer, Cham. https://doi.org/10.1007/978-3-319-97088-2_7

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  • DOI: https://doi.org/10.1007/978-3-319-97088-2_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-97087-5

  • Online ISBN: 978-3-319-97088-2

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