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In this paper, we focus on the edit distance between two given strings where block-edit operations are allowed and better fitting to the human natural edit behaviors.
The core of the soft dictionary matching method is calculating the similarity between two given strings (one as query, the other as pattern) using appropriate distance metrics.
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Let s be the given string.
The p-spectrum kernel between two strings is given by the corresponding Euclidean dot product (Leslie et al., 2002).
The relationship between the presence of such matching q-grams (sequence of length q) and the edit distance was revealed in a seminal paper by Ukkonen (1992): a lower bound for the edit distance between two strings is given by the L1 distance between their count vectors of q-grams (for q=3 these are the trinucleotide frequency vectors).
Crossover is considered as the partial exchange of corresponding segments between two parent strings to produce two offspring strings.
The minimum edit distance between two gene strings in DNA sequences represents the number of editing operations (insertion, deletion or substitution) between the query and original strings.
Hamming distance is the number of bits which differ between two binary strings.
Levenshtein distance [ 74] is a measure of the similarity between two strings, which we will refer to as the source string (s) and the target string (t).
One lower bound on the edit distance between two strings is the difference in the lengths of the two strings.
During the first training phase (string discrimination training), the birds had to discriminate between two strings.
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