- 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 = "
http://gpbib.cs.ucl.ac.uk/gecco2005/docs/p1761.pdf",
-
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",
