Molecular Learning of wDNF Formulae

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Zhang:2005:DNA,

• author = "Byoung-Tak Zhang and Ha-Young Jang",
• title = "Molecular Learning of {wDNF} Formulae",
• booktitle = "11th International Workshop on DNA Computing, DNA11",
• year = "2006",
• editor = "Alessandra Carbone and Niles A. Pierce",
• volume = "3892",
• series = "Lecture Notes in Computer Science",
• pages = "427--437",
• address = "London, Ontario, Canada",
• month = "6-9 " # jun,
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming, Disjunctive Normal Form, Hybridization Reaction, Query Pattern, Diagnosis Problem",
• isbn13 = "978-3-540-34161-1",
• URL = "https://rdcu.be/dh4fR",
• DOI = "doi:10.1007/11753681_34",
• size = "11 pages",
• abstract = "We introduce a class of generalized DNF formulae called wDNF or weighted disjunctive normal form, and present a molecular algorithm that learns a wDNF formula from training examples. Realized in DNA molecules, the wDNF machines have a natural probabilistic semantics, allowing for their application beyond the pure Boolean logical structure of the standard DNF to real-life problems with uncertainty. The potential of the molecular wDNF machines is evaluated on real-life genomics data in simulation. Our empirical results suggest the possibility of building error-resilient molecular computers that are able to learn from data, potentially from wet DNA data.",

}

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

Citations