By Gregory Alegi

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P. S. P. ), Pattern Recognition, Machine Intelligence and Biometrics © Higher Education Press, Beijing and Springer-Verlag Berlin Heidelberg 2011 4 1 A Review of Applications of Evolutionary Algorithms in Pattern Recognition another image with raw pixel data, constituting either the boundary of a region or all the points in the region itself. Feature selection deals with extracting features for differentiating one class of objects from another. The output of this stage is a vector of values of the measured features.

3) Uniform crossover This operator was proposed by Syswerda [43] and can be seen as a generalization of the two previous crossover techniques. 2 Basic Notions of Evolutionary Algorithms 9 this case, for each bit in the first offspring it decides (with some probability Pc ) which parent will contribute its value in that position, as indicated in Fig. 6. The second offspring will receive the bit from the other parent. Although for some problems uniform crossover presents several advantages over other crossover techniques [43], in general, one-point crossover seems to be a bad choice, but there is no clear winner between two-point and uniform crossover [44, 34].

The authors indicated that their approach can be used for the segmentation of gray-scale, color and textural images. Indeed, they indicated that their approach can be extended to any vector-valued parametric images, regardless of their number of components. Krawiec et al. provided in [77] a review of work on the use of genetic programming (GP) [22] for object detection and image analysis. As indicated before, GP refers to a variation of the genetic algorithm in which a tree-encoding is adopted.

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Ansaldo A.1 Balilla by Gregory Alegi


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