Edit metric decoding: a new hope

Authors:
Joseph A. Brown;Sheridan K. Houghten;Daniel A. Ashlock
Affiliations:
Brock University, St. Catharines, Ontario, Canada;Brock University, St. Catharines, Ontario, Canada;University of Guelph, Guelph, Ontario, Canada
Venue:
C3S2E '09 Proceedings of the 2nd Canadian Conference on Computer Science and Software Engineering
Year:
2009

Citing 2
Cited 0

The String-to-String Correction Problem

Journal of the ACM (JACM)
Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning

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Abstract

In this position paper we examine preliminary results of a new type of general error correction decoder for Edit Metric Codes. The Single Classifier Machine Decoder uses the concept of Side Effect Machines(SEMs) created via Genetic Algorithms(GAs) in order to create a mapping from the Edit Metric to the Euclidean Metric to create a decoder. By not having to measure the edit distance to every codeword the decoder has a far smaller runtime complexity. Fuzzy versions are also examined which reduce the number of times the Levenshtein or Edit distance must be calculated. Codes of the form (n, M, d)4 are targeted due to their suitability for bioinformatics problems. A (12, 55, 7)4 code is used as an example of the process.