Dynamic Algorithm Composition for Image Segmentation

In previous work, algorithms have been decomposed into basic algorithmic components and then recomposed into brand new algorithms using a genetic algorithm. The algorithm is composed in full prior to execution of the algorithm. We refer to this as static algorithm composition (SAC). This study examines composing segmentation algorithms in real-time by varying the techniques used for each of the algorithmic components as well as the order of the algorithmic components at different points during the execution of the segmentation algorithm. A genetic algorithm (GA) is used to compose the segmentation algorithm. Furthermore, the techniques that can be used for each algorithmic component changes every $g$ generations of the GA. A selection perturbative hyper-heuristic is used to determine the techniques that the GA can use for each of the algorithmic components. We refer to this as dynamic algorithm composition (DAC). Static and dynamic algorithm composition is evaluated for image segmentation using the BSD-500, PASCAL VOC, Football and COVID CT scan datasets. DAC improved on SAC for all datasets. Additionally, DAC was found to be competitive with the state of the art and improved on the best known results for the COVID CT scan datasets.