The Building Block Hypothesis suggests that Genetic Algorithms (GAs) are well-suited for hierarchical problems, where efficient solving requires proper problem decomposition and assembly of solution from sub-solution with strong non-linear interdependencies. The paper proposes a hill-climber operating over the building block (BB) space that can efficiently address hierarchical problems. The new Building Block Hill-Climber (BBHC) uses past hill-climb experience to extract BB information and adapts its neighborhood structure accordingly. The perpetual adaptation of the neighborhood structure allows the method to climb the hierarchical structure solving successively the hierarchical levels. It is expected that for fully non deceptive hierarchical BB structures the BBHC can solve hierarchical problems in linearithmic time. Empirical results confirm that the proposed method scales almost linearly with the problem size thus clearly outperforms population based recombinative methods.

Several hundreds of thousand humans are diagnosed with brain cancer every year, and the majority dies within the next two years. The chances of survival could be easiest improved by early diagnosis. This is why there is a strong need for reliable algorithms that can detect the presence of gliomas in their early stage. While an automatic tumor detection algorithm can support a mass screening system, the precise segmentation of the tumor can assist medical staff at therapy planning and patient monitoring. This paper presents a random forest based procedure trained to segment gliomas in multispectral volumetric MRI records. Beside the four observed features, the proposed solution uses 100 further features extracted via morphological operations and Gabor wavelet filtering. A neighborhood-based post-processing was designed to regularize and improve the output of the classifier. The proposed algorithm was trained and tested separately with the 54 low-grade and 220 high-grade tumor volumes of the MICCAI BRATS 2016 training database. For both data sets, the achieved accuracy is characterized by an overall mean Dice score > 83%, sensitivity > 85%, and specificity > 98%. The proposed method is likely to detect all gliomas larger than 10 mL.

Inherent networks of potential energy surfaces proposed in physical chemistry inspired a compact network characterization of combinatorial fitness landscapes. In these so-called Local Optima Networks (LON), the nodes correspond to the local optima and the edges quantify a measure of adjacency -transition probability between them.Methods so far used an exhaustive search for extracting LON, limiting their applicability to small problem instances only. To increase scalability, in this paper a new data-driven methodology is proposed that approximates the LON from actual runs of search methods. The method enables the extraction and study of LON corresponding to the various types of instances from the Quadratic Assignment Problem Library (QAPLIB), whose search spaces are characterized in terms of local minima connectivity. Our analysis provides a novel view of the unified testbed of QAP combinatorial landscapes used in the literature, revealing qualitative inherent properties that can be used to classify instances and estimate search difficulty.

No abstract

Curve skeletons are used for linear representation of 3D objects in a wide variety of engineering and medical applications. The outstandingly robust and flexible curve skeleton extraction algorithm, based on generalized potential fields, suffers from seriously heavy computational burden. In this paper we propose and evaluate a hierarchical formulation of the algorithm, which reduces the space where the skeleton is searched, by excluding areas that are unlikely to contain relevant skeleton branches. The algorithm was evaluated using dozens of object volumes. Tests revealed that the computational load of the skeleton extraction can be reduced up to 100 times, while the accuracy doesn't suffer relevant damage.