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Just because it works: a response to comments on “On the Mapping of Genotype to Phenotype in Evolutionary Algorithms”

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Abstract

This response examines the context and implications of the comments to "On the Mapping of Genotype to Phenotype in Evolutionary Algorithms" that appears in this journal. The notion of metaphor is first considered and then the general themes of the commentaries addressed. The response subsequently focuses on representation and operators, noting that many of the comments support our basic premise.

The main conclusion is that Sterelny's conditions do form a suitable basis for representation and operator design and that the collection of responses form an excellent basis for further discussion and research in evolutionary computation.

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References

  1. J.A. Foster, Taking “biology” just seriously enough: Commentary on “On the mapping of genotype to phenotype in evolutionary algorithms” by Peter A. Whigham, Grant Dick, and James Maclaurin. Genet. Program. Evolvable. Mach. (2017). doi:10.1007/s10710-017-9296-x

  2. L. Altenberg, 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. Genet. Program. Evolvable. Mach. (2017). doi:10.1007/s10710-017-9290-3

  3. A. Ekárt, P.R. Lewis, Genotype–phenotype mapping implications for genetic programming representation: Commentary on “On the mapping of genotype to phenotype in evolutionary algorithms” by Peter A. Whigham, Grant Dick, and James Maclaurin. Genet. Program. Evolvable. Mach. (2017). doi:10.1007/s10710-017-9291-2

  4. D.B. Kell, Evolutionary algorithms and synthetic biology for directed evolution: Commentary on “On the mapping of genotype to phenotype in evolutionary algorithms” by Peter A. Whigham, Grant Dick, and James Maclaurin. Genet. Program. Evolvable. Mach. (2017). doi:10.1007/s10710-017-9292-1

  5. M. O’Neill, M. Nicolau, Commentary on “On the mapping of genotype to phenotype in evolutionary algorithms” by Whigham, Dick and Maclaurin. Genet. Program. Evolvable. Mach. (2017). doi:10.1007/s10710-017-9293-0

  6. C. Ryan, A rebuttal to Whigham, Dick, and Maclaurin by one of the inventors of grammatical evolution: Commentary on “On the mapping of genotype to phenotype in evolutionary algorithms” by Peter A. Whigham, Grant Dick, and James Maclaurin. Genet. Program. Evolvable. Mach. (2017). doi:10.1007/s10710-017-9294-z

  7. G. Squillero, A. Tonda, (Over-)Realism in evolutionary computation: Commentary on “On the mapping of genotype to phenotype in evolutionary algorithms” by Peter A. Whigham, Grant Dick, and James Maclaurin. Genet. Program. Evolvable. Mach. (2017). doi:10.1007/s10710-017-9295-y

  8. K. Sörensen, Metaheuristics—the metaphor exposed. Int. Trans. Oper. Res. 22(1), 3–18 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  9. R.C. Lewontin, The units of selection. Annu. Rev. Ecol. Syst. 1, 1–18 (1970)

    Article  Google Scholar 

  10. K. Sterelny, Niche construction, developmental systems and the extended replicator, in Cycles of Contingency: Developmental Systems and Evolution, ed. by S. Oyama, R.D. Gray, P.E. Griffiths (MIT Press, Cambridge, Mass, 2001)

  11. R. Poli, A simple but theoretically-motivated method to control bloat in genetic programming, in European Conference on Genetic Programming (Springer, 2003)

  12. I. Rechenberg, Evolutionsstrategie: Optimierung Technischer Systeme Nach Prinzipien der Biologischen Evolution (Frommann-Holzboog, Stuttgart, 1973)

    Google Scholar 

  13. C. Ryan, J. Collins, M.O. Neill, Grammatical evolution: Evolving programs for an arbitrary language, in Genetic Programming: First European Workshop, EuroGP’98 Paris, France, April 14–15, 1998 Proceedings, ed. by W. Banzhaf, et al. (Springer, Berlin, Heidelberg, 1998) pp. 83–96

  14. A. Brabazon, M. O’Neill, S. McGarraghy, Natural Computing Algorithms (Springer-Verlag, Berlin, Heidelberg, 2015)

  15. L. Davis, Hybridization and numerical representation, in Handbook of Genetic Algorithms, ed. by L. Davis (Van Nostrand Reinhold, New York, 1991) pp. 61–71

  16. D. Goldberg, Real-coded genetic algorithms, virtual alphabets, and blocking. Complex Syst 5(2), 139–167 (1991)

    MathSciNet  MATH  Google Scholar 

  17. C.Z. Janikow, Z. Michalewicz, An experimental comparison of binary and floating point representations in genetic algorithms, in Proceedings of the Fourth International Conference on Genetic Algorithms, ed. by R.K. Belew, L.B. Booker (Morgan Kaufmann, San Diego, CA, 1991) pp. 31–36

  18. A.H. Wright, Genetic algorithms for real parameter optimization, in Foundations of Genetic Algorithms, ed. by G.J.E. Rawlins (Morgan Kaufmann, San Mateo, California, 1991) pp. 205–218

  19. P.A. Whigham, G. Dick, J. Maclaurin, On the Mapping of Genotype to Phenotype in Evolutionary Algorithms. Genetic Programming and Evolvable Machines, 2016. TBA

  20. P.A. Whigham et al., Examining the “Best of Both Worlds” of grammatical evolution, in Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation (ACM, Madrid, Spain, 2015), pp. 1111–1118

  21. J. McDermott et al., Genetic programming needs better benchmarks, in Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation (ACM, Philadelphia, Pennsylvania, USA, 2012) pp. 791–798

  22. D.R. White et al., Better GP benchmarks: community survey results and proposals. Genet. Program. Evol. Mach. 14(1), 3–29 (2013)

    Article  Google Scholar 

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Acknowledgements

Special thanks must go to Lee Spector, Editor-in-Chief of Genetic Programming and Evolvable Machines, for managing the submission and editorial process for this discussion. His efforts in streamlining this process have been greatly appreciated.

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Authors and Affiliations

  1. Department of Information Science, University of Otago, Dunedin, New Zealand

    Peter A. Whigham & Grant Dick

  2. Department of Philosophy, University of Otago, Dunedin, New Zealand

    James Maclaurin

Authors
  1. Peter A. Whigham
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  2. Grant Dick
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  3. James Maclaurin
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Correspondence to Peter A. Whigham.

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Whigham, P.A., Dick, G. & Maclaurin, J. Just because it works: a response to comments on “On the Mapping of Genotype to Phenotype in Evolutionary Algorithms”. Genet Program Evolvable Mach 18, 399–405 (2017). https://doi.org/10.1007/s10710-017-9289-9

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