Evolution of Biological Information

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Thomas D. Schneider",
  title =        "Evolution of Biological Information",
  journal =      "Nucleic Acids Research",
  year =         "2000",
  volume =       "28",
  number =       "14",
  pages =        "2794--2799",
  email =        "toms@ncifcrf.gov",
  keywords =     "genetic algorithms, genetic programming, artificial
                 life, alife",
  URL =          "https://doi.org/10.1093/nar/28.14.2794",
  URL =          "http://alum.mit.edu/www/toms/paper/ev/",
  URL =          "http://www.ccrnp.ncifcrf.gov/~toms/paper/ev/ev.pdf",
  DOI =          "doi:10.1093/nar/28.14.2794",
  abstract =     "How do genetic systems gain information by
                 evolutionary processes? Answering this question
                 precisely requires a robust, quantitative measure of
                 information. Fortunately, fifty years ago Claude
                 Shannon defined information as a decrease in the
                 uncertainty of a receiver. For molecular systems,
                 uncertainty is closely related to entropy and hence has
                 clear connections to the Second Law of Thermodynamics.
                 These aspects of information theory have allowed the
                 development of a straightforward and practical method
                 of measuring information in genetic control systems.
                 Here this method is used to observe information gain in
                 the binding sites for an artificial `protein' in a
                 computer simulation of evolution. The simulation begins
                 with zero information and, as in naturally occurring
                 genetic systems, the information measured in the fully
                 evolved binding sites is close to that needed to locate
                 the sites in the genome. The transition is rapid,
                 demonstrating that information gain can occur by
                 punctuated equilibrium.",
  size =         "6 pages",
  notes =        "This is a *true* genetic algorithm in the sense that
                 it uses the genetic algorithm method to show how
                 information gain occurs in living organisms.

                 'Roman arch' - stands up after scaffolding is removed.
                 onemax, unitation. Coevolution of binding sites and
                 recogniser gene.",

Genetic Programming entries for Thomas D Schneider