Program Evolvability Under Environmental Variations and Neutrality

Created by W.Langdon from gp-bibliography.bib Revision:1.4221

  author =       "Tina Yu",
  title =        "Program Evolvability Under Environmental Variations
                 and Neutrality",
  booktitle =    "Proceedings 9th European Conference on Artificial
                 Life, ECAL 2007",
  year =         "2007",
  editor =       "Fernando {Almeida e Costa} and Luis Mateus Rocha and 
                 Ernesto Costa and Inman Harvey and Antonio Coutinho",
  volume =       "4648",
  series =       "Lecture Notes in Computer Science",
  pages =        "835--844",
  address =      "Lisbon",
  month =        sep # " 10-14",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-540-74913-4",
  URL =          "",
  DOI =          "doi:10.1007/978-3-540-74913-4_84",
  size =         "10 pages",
  abstract =     "Biological organisms employ various mechanisms to cope
                 with the dynamic environments they live in. One recent
                 research reported that depending on the rates of
                 environmental variation, populations evolve toward
                 genotypes in different regions of the neutral networks
                 to adapt to the changes. Inspired by that work, we used
                 a genetic programming system to study the evolution of
                 computer programs under environmental variation.
                 Similar to biological evolution, the genetic
                 programming populations exploit neutrality to cope with
                 environmental fluctuations and evolve evolvability. We
                 hope this work sheds new light on the design of
                 open-ended evolutionary systems which are able to
                 provide consistent evolvability under variable
  notes =        "EQ only function set {\citelangdon:1998:BBparity} even
                 4-bit parity v. 4 bit always on.

                 p836 'variation is the fuel of evolution'. p387 at the
                 edge of neutral 'network mutations are likely to
                 produce different phenotype'. No crossover. No length
                 changes? Solution is exactly twice as likely to be
                 selected as non-solution. I.e. always-on is no worse
                 than anything else when selecting for even-4-parity.
                 p842 longer solutions have proportionally more
                 mutations. p843 ???All better programs have the maximum
                 size (18) ???",

Genetic Programming entries for Tina Yu