- author = "Brian J. Ross",
- title = "Probabilistic Pattern Matching and the Evolution of Stochastic Regular Expressions",
- journal = "Applied Intelligence",
- year = "2000",
- volume = "13",
- number = "3",
- pages = "285--300",
- month = nov,
- publisher = "Springer Netherlands",
- keywords = "genetic algorithms, genetic programming, stochastic regular expressions, Computer Science",
- ISSN = "0924-669X",
-
URL = "
http://www.cosc.brocku.ca/~bross/research/apin1303.pdf",
-
URL = "
https://rdcu.be/cHQTB",
-
DOI = "
doi:10.1023/A:1026524328760",
- size = "16 pages",
- abstract = "The use of genetic programming for probabilistic pattern matching is investigated. A stochastic regular expression language is used. The language features a statistically sound semantics, as well as a syntax that promotes efficient manipulation by genetic programming operators. An algorithm for efficient string recognition based on approaches in conventional regular language recognition is used. When attempting to recognise a particular test string, the recognition algorithm computes the probabilities of generating that string and all its prefixes with the given stochastic regular expression. To promote efficiency, intermediate computed probabilities that exceed a given cut-off value will pre-empt particular interpretation paths, and hence prune unconstructive interpretation. A few experiments in recognising stochastic regular languages are discussed. Application of the technology in bioinformatics is in progress.",
-
notes = "SRE, definite clause translation grammar DCTG-GP,
Prolog. p295 chi-squared fitness test. Tomita RE
language. 'A test set is generated before every
generation'. stochastic regular language L2.R.C.
Carrasco and M.L. Forcada
Also known as \cite{springerlink:10.1023/A:1026524328760}",
