Skip to main content

Advertisement

Log in

Distilling the salient features of natural systems: Commentary on “On the mapping of genotype to phenotype in evolutionary algorithms” by Whigham, Dick and Maclaurin

  • Commentary
  • Published:
Genetic Programming and Evolvable Machines Aims and scope Submit manuscript

A Reply to this article was published on 24 February 2017

The Original Article was published on 23 February 2017

Abstract

Here we comment on the article, “On the mapping of genotype to phenotype in evolutionary algorithms”, by Peter A. Whigham, Grant Dick, and James Maclaurin. The authors present a critical view on the use of genotype to phenotype mapping in Evolutionary Algorithms, and how the use of this analogy can be detrimental for problem solving. They examine a grammar-based approach to Genetic Programming (GP), Grammatical Evolution (GE), and highlight properties of GE which are detrimental to effective evolutionary search. Rather than use loose analogies and methaphors, we suggest that a focus should be (and has been in GE and other approaches to GP) on addressing one of the most significant open issues in our field, i.e., What are the sufficient set of features in natural, genetic, evolutionary and developmental systems, which can translate into the most effective computational approaches for program synthesis?

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. J. Byrne, M. Nicolau, A. Brabazon, M. O’Neill, An examination of synchronisation in artificial gene regulatory networks. in Proceedings of IEEE CEC 2014 (IEEE Press, 2014), pp. 2764–2769

  2. D. Fagan, E. Hemberg, S. McGarraghy, M. O’Neill, Understanding expansion order and phenotypic connectivity in \(\pi\)GE. in Proceedings of EuroGP 2013 (Springer, 2013), pp. 37–48

  3. R. Harper, GE, Explosive grammars and the lasting legacy of bad initialisation. in IEEE Congress on Evolutionary Computation—CEC 2010 (IEEE press, 2011) pp. 2602–2609

  4. D.R. Hofstadter, Gödel, Escher, Bach: An Eternal Golden Braid (Penguin Books, London, 1979)

    MATH  Google Scholar 

  5. S.A. Kauffman, The Origins of Order—Self-Organization and Selection in Evolution (Oxford University Press, Oxford, 1993)

    Google Scholar 

  6. M. Keijzer, Improving symbolic regression with interval arithmetic and linear scaling, in European Conference on Genetic Programming—EuroGP 2003, ed. by Ryan, et al. (Springer, 2003), pp. 70–82

  7. J.R. Koza, Genetic Programming: On the Programming of Computers by Means of Natural Evolution (MIT Press, Cambridge, 1992)

    MATH  Google Scholar 

  8. S. Luke, L. Panait, Is the perfect the enemy of the good, in Genetic and Evolutionary Computation—GECCO 2002, ed. by Langdon, et al. (Morgan Kaufmann, 2002), pp. 820–828

  9. A. Moraglio, K. Krawiec, C.G. Johnson, Geometric semantic genetic programming. in Parallel Problem Solving from Nature—PPSN XII (Springer, 2012), pp. 21–31

  10. A. Moraglio, J. McDermott, M. O’Neill, Geometric semantic grammatical evolution. in Proceedings of PPSN 2014 Workshops (2014)

  11. E. Murphy, M. Nicolau, E. Hemberg, M. O’Neill, A. Brabazon, Differential gene expression with tree-adjunct grammars. in PPSN XII 2012, part 1 (Springer 2012), pp. 377–386.

  12. M. Nicolau, Automatic grammar complexity reduction in grammatical evolution. in Genetic and Evolutionary Computation Workshops—GECCO 2004, ed by Poli et al. (2004)

  13. M. Nicolau, I. Dempsey, Introducing grammar based extensions for grammatical evolution. in IEEE Congress on Evolutionary Computation—CEC 2006 (IEEE press, 2006), pp. 2663–2670

  14. M. Nicolau, D. Costelloe, Using grammatical evolution to parameterise interactive 3D image generation, in Applications of Evolutionary Computation–EvoApplications 2011, ed. by Di Chio, et al. (Springer, Berlin, 2011), pp. 374–383

    Google Scholar 

  15. M. Nicolau, M. O’Neill, A. Brabazon, Termination in grammatical evolution: grammar design, wrapping, and tails. in IEEE Congress on Evolutionary Computation—CEC 2012 (IEEE press, 2012)

  16. M. O’Neill, C. Ryan, Grammatical Evolution—Evolutionary Automatic Programming in an Arbitrary Language (Kluwer, Dordrecht, 2003)

    MATH  Google Scholar 

  17. M. O’Neill, A. Brabazonm, M. Nicolau, S. McGarraghy, P. Keenan, \(\pi\) Grammatical evolution. in Proceedings of GECCO 2004, Vol. II (Springer, 2004), pp. 617–629

  18. M. O’Neill, L. Vanneschi, S. Gustafson, W. Banzhaf, Open issues in genetic programming. Genet. Program Evol Mach 11(3/4), 339–363 (2010)

    Article  Google Scholar 

  19. D. Perez, M. Nicolau, M. O’Neill, A. Brabazon, Reactiveness and navigation in computer games: different needs, different approaches. in IEEE Conference on Computation Intelligence and Games-CIG 2011 (IEEE press, 2011)

  20. K. Sörensen, Metaheuristics—the metaphor exposed. Int. Trans. Op. Res. 22(1), 3–18 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  21. K. Sterelny, Niche construction, developmental systems and the extended replicator, in Cycles of Contingency: Developmental Systems and Evolution, ed. by S. Oyama, et al. (MIT Press, Cambridge, 2001)

    Google Scholar 

  22. P.A. Whigham, G. Dick, J. Maclaurin, C.A. Owen, Examining the “best of both worlds” of grammatical evolution, in Genetic and Evolutionary Computation—GECCO 2015, ed. by Sara Silva (ACM, 2015), pp. 1111–1118

  23. A. Wagner, Robustness and Evolvability in Living Systems (Princeton University Press, Princeton, 2005)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Michael O’Neill.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

O’Neill, M., Nicolau, M. Distilling the salient features of natural systems: Commentary on “On the mapping of genotype to phenotype in evolutionary algorithms” by Whigham, Dick and Maclaurin. Genet Program Evolvable Mach 18, 379–383 (2017). https://doi.org/10.1007/s10710-017-9293-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10710-017-9293-0

Keywords

Navigation