By Matthias Schmidt
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Finally, deformation grammars can be used. e. the distance between strings which correspond to the unknown object and the object class patterns. Further, determined distances are analysed and the recognized object belongs to the class from which the string has the shortest distance. Specific methods (Levenshtein distance Ld(s, t), Needleman-Wunsch method) can be used to determine the distance between attribute descriptions of image (Gusfield, 1997). g. in (Minařík, Šťastný & Popelka, 2008). If the parameters of these methods are correctly set, these methods provide good rate of successful identified objects with excellent classification speed.
Fitness computation No Finish Desired fitness reached ? Crossover Mutation Yes No Crossover + Selection Selection Fig. 6. Flowchart of two-level grammatical evolution The first level of the optimization is performed using grammatical evolution. According to the grammar, the output can be a function containing variables (x in our case); and instead of directly generating numbers using the
G. Cocke-Younger-Kasami algorithm (Kasami, 1965), which grants that the time of analysis is proportional to third power of string length; Top-down parsing – We begin from initial symbol and we are trying to generate analysed string. String generated so far is saved in the stack. Every time a terminal symbol appears on the top of the stack, it is compared to actual input symbol of the analysed string. If symbols are identical, the terminal symbol is removed from the top of the stack. g. with help of backtracking).