Automatic molecular design using evolutionary techniques

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

  title =        "Automatic molecular design using evolutionary
  author =       "Al Globus and John Lawton and Todd Wipke",
  journal =      "Nanotechnology",
  volume =       "10",
  number =       "3",
  month =        sep,
  year =         "1999",
  pages =        "290--299",
  URL =          "",
  URL =          "",
  URL =          "",
  URL =          "",
  DOI =          "doi:10.1088/0957-4484/10/3/312",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "Molecular nanotechnology is the precise,
                 three-dimensional control of materials and devices at
                 the atomic scale. An important part of nanotechnology
                 is the design of molecules for specific purposes. This
                 paper describes early results using genetic software
                 techniques to automatically design molecules under the
                 control of a fitness function. The fitness function
                 must be capable of determining which of two arbitrary
                 molecules is better for a specific task. The software
                 begins by generating a population of random molecules.
                 The individual molecules in a population are then
                 evolved towards greater fitness by randomly combining
                 parts of the better existing molecules to create new
                 molecules. These new molecules then replace some of the
                 less fit molecules in the population. We apply a unique
                 genetic crossover operator to molecules represented by
                 graphs, i.e., sets of atoms and the bonds that connect
                 them. We present evidence suggesting that crossover
                 alone, operating on graphs, can evolve any possible
                 molecule given an appropriate fitness function and a
                 population containing both rings and chains. Most prior
                 work evolved strings or trees that were subsequently
                 processed to generate molecular graphs. In principle,
                 genetic graph software should be able to evolve other
                 graph-representable systems such as circuits,
                 transportation networks, metabolic pathways, and
                 computer networks.",

Genetic Programming entries for Al Globus John Lawton Todd Wipke