Analysis of Grammatical Evolution Approaches to Regular Expression Induction

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Gonzalez-Pardo:2011:AoGEAtREI,

• title = "Analysis of Grammatical Evolution Approaches to Regular Expression Induction",
• author = "Antonio Gonzalez-Pardo and David Camacho",
• pages = "632--639",
• booktitle = "Proceedings of the 2011 IEEE Congress on Evolutionary Computation",
• year = "2011",
• editor = "Alice E. Smith",
• month = "5-8 " # jun,
• address = "New Orleans, USA",
• organization = "IEEE Computational Intelligence Society",
• publisher = "IEEE Press",
• ISBN = "0-7803-8515-2",
• keywords = "genetic algorithms, genetic programming, grammatical evolution, Data mining",
• DOI = "doi:10.1109/CEC.2011.5949679",
• abstract = "Regular expressions, or regexes, have been used traditionally as a pattern matching tool to search for structures in a set of objects, like files, text documents or folders. Pattern matching can be used to look for files whose name contains a given string, to search files that contain a specific pattern within them, or simply to extract text in a set of documents. It is very popular to apply regexes to detect and extract patterns that represent phone numbers, URLs, email addresses, etc. These kind of information can be characterised because it has a well defined structure. Nevertheless, regexes are not very frequently used because its high complexity in both, syntax and grammatical rules, makes regexes difficult to understand. For this reason, the development of programs able to automatically generate, and evaluate, regexes has become a valuable task. This work analyses the performance of different grammatical evolutionary approaches in the generation of regexes able to extract URL patterns. Four different types of grammars have been evaluated: a context-free grammar, a context-free grammar with a penalised fitness function, an extensible context-free grammar, and a Christiansen grammar. For the considered problem, the experimental results show that the best performance of the system, measured as cumulative success rate, is achieved using Christiansen grammars.",
• notes = "CEC2011 sponsored by the IEEE Computational Intelligence Society, and previously sponsored by the EPS and the IET.",

}

Genetic Programming entries for Antonio Gonzalez-Pardo David Camacho

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