Linear Genetic Programming using a compressed genotype representation

Parent, Johan; Nowé, Ann; Steenhaut, Kris; Defaweux, Anne

doi:10.1109/cec.2005.1554822

Cited by 2 publications

(4 citation statements)

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“…Representation: In most of the approaches, GP individuals are represented as a tree structure. Moreover, the linear representation for GP, which is also constructed using functions and terminals, is employed in a few works 37 …”

Section: Terminologies Used In Gpmentioning

confidence: 99%

“…In another technique, Parent et al 37 proposed lossless compression of medical images. They used a linear GP driven by compressed GA (cGA) and found a transformation, represented as T ( d ), which improved the compression ratio of data d .…”

Section: Category‐wise Image Processing Applications Of Gpmentioning

confidence: 99%

“…In most of the approaches, GP individuals are represented as tree structure. Moreover, linear representation for GP, which is constructed with the help of functions and a terminal set, is also reported in some papers [17]. Function Set: Function set is chosen according to the problem at hand.…”

Section: Representationmentioning

confidence: 99%

“…Figure 7 depicts the steps of Fukunage's method. In another technique, Parent et al [17] proposed lossless compression of medical images. They used a linear GP driven by cGA and found a transformation, represented as T(d), which improved the compression ratio of data d. Moreover, this transformation could remove certain types of redundancy.…”

Section: Gp In Image Compressionmentioning

confidence: 99%

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A recent survey on the applications of genetic programming in image processing

Khan

Qureshi

Wahab

et al. 2021

Computational Intelligence

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Genetic programming (GP) has been primarily used to tackle optimization, classification, and feature selection related tasks. The widespread use of GP is due to its flexible and comprehensible tree‐type structure. Similarly, research is also gaining momentum in the field of image processing, because of its promising results over vast areas of applications ranging from medical image processing to multispectral imaging. Image processing is mainly involved in applications such as computer vision, pattern recognition, image compression, storage, and medical diagnostics. This universal nature of images and their associated algorithm, that is, complexities, gave an impetus to the exploration of GP. GP has thus been used in different ways for image processing since its inception. Many interesting GP techniques have been developed and employed in the field of image processing, and consequently, we aim to provide the research community an extensive view of these techniques. This survey thus presents the diverse applications of GP in image processing and provides useful resources for further research. In addition, the comparison of different parameters used in different applications of image processing is summarized in tabular form. Moreover, analysis of the different parameters used in image processing related tasks is carried‐out to save the time needed in the future for evaluating the parameters of GP. As more advancement is made in GP methodologies, its success in solving complex tasks, not only in image processing but also in other fields, may increase. In addition, guidelines are provided for applying GP in image processing related tasks, the pros and cons of GP techniques are discussed, and some future directions are also set.

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Section: Terminologies Used In Gpmentioning

confidence: 99%

Section: Category‐wise Image Processing Applications Of Gpmentioning

confidence: 99%

Section: Representationmentioning

confidence: 99%

Section: Gp In Image Compressionmentioning

confidence: 99%

See 2 more Smart Citations