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.
Similar content being viewed by others
References
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
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
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
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
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
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
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
K. Sörensen, Metaheuristics—the metaphor exposed. Int. Trans. Oper. Res. 22(1), 3–18 (2015)
R.C. Lewontin, The units of selection. Annu. Rev. Ecol. Syst. 1, 1–18 (1970)
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)
R. Poli, A simple but theoretically-motivated method to control bloat in genetic programming, in European Conference on Genetic Programming (Springer, 2003)
I. Rechenberg, Evolutionsstrategie: Optimierung Technischer Systeme Nach Prinzipien der Biologischen Evolution (Frommann-Holzboog, Stuttgart, 1973)
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
A. Brabazon, M. O’Neill, S. McGarraghy, Natural Computing Algorithms (Springer-Verlag, Berlin, Heidelberg, 2015)
L. Davis, Hybridization and numerical representation, in Handbook of Genetic Algorithms, ed. by L. Davis (Van Nostrand Reinhold, New York, 1991) pp. 61–71
D. Goldberg, Real-coded genetic algorithms, virtual alphabets, and blocking. Complex Syst 5(2), 139–167 (1991)
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
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
P.A. Whigham, G. Dick, J. Maclaurin, On the Mapping of Genotype to Phenotype in Evolutionary Algorithms. Genetic Programming and Evolvable Machines, 2016. TBA
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
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
D.R. White et al., Better GP benchmarks: community survey results and proposals. Genet. Program. Evol. Mach. 14(1), 3–29 (2013)
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.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
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
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10710-017-9289-9