Advertisement

Genetic Programming and Evolvable Machines

, Volume 18, Issue 3, pp 363–367 | Cite as

Probing the axioms of evolutionary algorithm design: Commentary on “On the mapping of genotype to phenotype in evolutionary algorithms” by Peter A. Whigham, Grant Dick, and James Maclaurin

  • Lee Altenberg
Commentary
Part of the following topical collections:
  1. Mapping of Genotype to Phenotype in Evolutionary Algorithms

Abstract

Properties such as continuity, locality, and modularity may seem necessary when designing representations and variation operators for evolutionary algorithms, but a closer look at what happens when evolutionary algorithms perform well reveals counterexamples to such schemes. Moreover, these variational properties can themselves evolve in sufficiently complex open-ended systems. These properties of evolutionary algorithms remain very much open questions.

Keywords

Fisher’s geometric model 1/5 rule Evolution of evolvability 

Notes

Acknowledgements

This work was supported by the Konrad Lorenz Institute for Evolution and Cognition Research, the Mathematical Biosciences Institute through National Science Foundation Award #DMS 0931642, the University of Hawai‘i at Mānoa, and the Stanford Center for Computational, Evolutionary and Human Genomics, Stanford University. I thank Marcus W. Feldman for his hospitality during a visit to his group.

References

  1. 1.
    L. Altenberg, The evolution of evolvability in genetic programming, in Advances in Genetic Programming, chapter 3, ed. by K.E. Kinnear (MIT Press, Cambridge, MA, 1994), pp. 47–74Google Scholar
  2. 2.
    L. Altenberg, Genome growth and the evolution of the genotype–phenotype map, in Evolution and Biocomputation: Computational Models of Evolution, ed. by W. Banzhaf, F.H. Eeckman. Lecture Notes in Computer Science, vol. 899 (Springer, Berlin, 1995), pp. 205–259Google Scholar
  3. 3.
    L. Altenberg, The schema theorem and Price’s theorem, in Foundations of Genetic Algorithms 3, ed. by D. Whitley, M.D. Vose (Morgan Kaufmann, San Mateo, 1995), pp. 23–49Google Scholar
  4. 4.
    L. Altenberg, Modularity in evolution: some low-level questions, in Modularity: Understanding the Development and Evolution of Natural Complex Systems, ed. by W. Callebaut, D. Rasskin-Gutman (MIT Press, Cambridge, 2005), pp. 99–128Google Scholar
  5. 5.
    E. Bornberg-Bauer, H.S. Chan, Modeling evolutionary landscapes: mutational stability, topology, and superfunnels in sequence space. Proc. Natl. Acad. Sci. 96(19), 10689–10694 (1999)CrossRefGoogle Scholar
  6. 6.
    M. Conrad, Molecular information processing in the central nervous system, in Physics and Mathematics of the Nervous System, ed. by M. Conrad, W. Güttinger, M. Dal Cin (Springer, Berlin, 1974), pp. 82–107Google Scholar
  7. 7.
    M. Conrad, The geometry of evolution. BioSystems 24(1), 61–81 (1990)CrossRefGoogle Scholar
  8. 8.
    A. Crombach, P. Hogeweg, Chromosome rearrangements and the evolution of genome structuring and adaptability. Mol. Biol. Evol. 24, 1130–1139 (2007)CrossRefGoogle Scholar
  9. 9.
    J. Draghi, G.P. Wagner, Evolution of evolvability in a developmental model. Evolution 62(2), 301–315 (2008)CrossRefGoogle Scholar
  10. 10.
    D.S. Fisher, A few comments ( & questions!) from a condensed matter physicist, 2014. Talk presented March 21, 2014 at The Simons Institute, Berkeley, Computational Theories of Evolution, https://simons.berkeley.edu/talks/wrap-up-session-general-discussion
  11. 11.
    R.A. Fisher, The Genetical Theory of Natural Selection (Clarendon Press, Oxford, 1930)CrossRefzbMATHGoogle Scholar
  12. 12.
    N. Kashtan, U. Alon, Spontaneous evolution of modularity and network motifs. Proc. Natl. Acad. Sci. USA 102, 13773–13778 (2005)CrossRefGoogle Scholar
  13. 13.
    W.B. Langdon, R. Poli, Fitness causes bloat, in 2nd On-line World Conference on Soft Computing in Engineering Design and Manufacturing (WSC2) (1997), pp. 1–10Google Scholar
  14. 14.
    L.A. Meyers, M. Lachmann, Evolution of genetic potential. PLoS Comput. Biol. 1, 236–243 (2005)Google Scholar
  15. 15.
    H.A. Orr, Adaptation and the cost of complexity. Evolution 54(1), 13–20 (2000)CrossRefGoogle Scholar
  16. 16.
    M.E. Palmer, M.W. Feldman, Survivability is more fundamental than evolvability. PloS ONE 7(6), e38025 (2012)CrossRefGoogle Scholar
  17. 17.
    I. Rechenberg, Evolutionsstrategie: Optimierung technischer Systeme nach Prinzipien der biologischen Evolution (Frommann-Holzboog, Stuttgart, 1973)Google Scholar
  18. 18.
    R.J. Riedl, A systems-analytical approach to macroevolutionary phenomena. Q. Rev. Biol. 52, 351–370 (1977)CrossRefGoogle Scholar
  19. 19.
    A. Singleton, N. Keenan, Defense against crossover, in Discussion in the Genetic Programming Workshop at the Fifth International Conference on Genetic Algorithms (1993)Google Scholar
  20. 20.
    K. Sterelny, Niche construction, developmental systems, and the extended replicator, in Cycles of Contingency: Developmental Systems and Evolution, ed. by S. Oyama, P.E. Griffiths, R.D. Gray (MIT Press Cambridge, MA, 2001), pp. 333–350Google Scholar
  21. 21.
    L. Valiant, Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World (Basic Books, New York, 2013)Google Scholar
  22. 22.
    E. van Nimwegen, J.P. Crutchfield, M. Huynen, Neutral evolution of mutational robustness. Proc. Natl. Acad. Sci. USA 96, 9716–9720 (1999)CrossRefGoogle Scholar
  23. 23.
    G.P. Wagner, L. Altenberg, Complex adaptations and the evolution of evolvability. Evolution 50(3), 967–976 (1996)CrossRefGoogle Scholar
  24. 24.
    P.A. Whigham, G. Dick, J. Maclaurin, On the mapping of genotype to phenotype in evolutionary algorithms. Genet. Program. Evolvable Mach. (2016)Google Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Information and Computer SciencesUniversity of Hawai‘i at MānoaHonoluluUSA
  2. 2.Konrad Lorenz Institute for Evolution and Cognition ResearchKlosterneuburgAustria

Personalised recommendations