- 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 = "
https://alum.mit.edu/www/toms/paper/ev/ev.pdf",
-
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.",
