Molecular programming: evolving genetic programs in a test tube

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Byoung-Tak Zhang and Ha-Young Jang",
  title =        "Molecular programming: evolving genetic programs in a
                 test tube",
  booktitle =    "{GECCO 2005}: Proceedings of the 2005 conference on
                 Genetic and evolutionary computation",
  year =         "2005",
  editor =       "Hans-Georg Beyer and Una-May O'Reilly and 
                 Dirk V. Arnold and Wolfgang Banzhaf and Christian Blum and 
                 Eric W. Bonabeau and Erick Cantu-Paz and 
                 Dipankar Dasgupta and Kalyanmoy Deb and James A. Foster and 
                 Edwin D. {de Jong} and Hod Lipson and Xavier Llora and 
                 Spiros Mancoridis and Martin Pelikan and Guenther R. Raidl and 
                 Terence Soule and Andy M. Tyrrell and 
                 Jean-Paul Watson and Eckart Zitzler",
  volume =       "2",
  ISBN =         "1-59593-010-8",
  pages =        "1761--1768",
  address =      "Washington DC, USA",
  URL =          "",
  DOI =          "doi:10.1145/1068009.1068301",
  publisher =    "ACM Press",
  publisher_address = "New York, NY, 10286-1405, USA",
  month =        "25-29 " # jun,
  organisation = "ACM SIGEVO (formerly ISGEC)",
  keywords =     "genetic algorithms, genetic programming, design, DNA
                 computing, genetic programs, in vitro evolution,
                 molecular evolutionary computation (MEC), molecular
                 programming (MP)",
  abstract =     "We present a molecular computing algorithm for
                 evolving DNA-encoded genetic programs in a test tube.
                 The use of synthetic DNA molecules combined with
                 biochemical techniques for variation and selection
                 allows for various possibilities for building novel
                 evolvable hardware. Also, the possibility of
                 maintaining a huge number of individuals and their
                 massively parallel manipulation allows us to make
                 robust decisions by the {"}molecular{"} genetic
                 programs evolved within a single population. We
                 evaluate the potentials of this {"}molecular
                 programming{"} approach by solving a medical diagnosis
                 problem on a simulated DNA computer. Here the
                 individual genetic program represents a decision list
                 of variable length and the whole population takes part
                 in making probabilistic decisions. Tested on a
                 real-life leukemia diagnosis data, the evolved
                 molecular genetic programs showed a comparable
                 performance to decision trees. The molecular
                 evolutionary algorithm can be adapted to solve problems
                 in biotechnology and nano-technology where the
                 physico-chemical evolution of target molecules is of
                 pressing importance.",
  notes =        "GECCO-2005 A joint meeting of the fourteenth
                 international conference on genetic algorithms
                 (ICGA-2005) and the tenth annual genetic programming
                 conference (GP-2005).

                 ACM Order Number 910052",

Genetic Programming entries for Byoung-Tak Zhang Ha-Young Jang