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.

中文翻译：

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最近关于遗传编程在图像处理中的应用的调查

遗传编程（GP）主要用于处理优化、分类和特征选择相关的任务。GP的广泛使用是由于其灵活且易于理解的树型结构。同样，图像处理领域的研究也在获得动力，因为它在从医学图像处理到多光谱成像的广泛应用领域取得了可喜的成果。图像处理主要涉及计算机视觉、模式识别、图像压缩、存储和医学诊断等应用。图像的这种普遍性及其相关算法，即复杂性，推动了对 GP 的探索。因此，GP 从一开始就以不同的方式用于图像处理。许多有趣的 GP 技术已被开发并应用于图像处理领域，因此，我们旨在为研究界提供这些技术的广泛视图。因此，本调查介绍了 GP 在图像处理中的各种应用，并为进一步研究提供了有用的资源。此外，以表格形式总结了在图像处理的不同应用中使用的不同参数的比较。此外，对图像处理相关任务中使用的不同参数进行分析，以节省未来评估 GP 参数所需的时间。随着 GP 方法取得更多进步，它在解决复杂任务方面的成功率可能会增加，不仅在图像处理方面，而且在其他领域。此外，