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


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.


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



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.


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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

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