David Iclanzan

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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(More)
Probabilistic model building methods can render difficult problems feasible by identifying and exploiting dependencies. They build a probabilistic model from the statistical properties of multiple samples (population) scattered in the search space and generate offspring according to this model. The memory requirements of these methods grow along with the(More)
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(More)
This paper presents a patient specific deformable heart model that involves the known electric and mechanic properties of the cardiac cells and tissue. The accuracy and efficiency of the algorithm was tested for anisotropic and inhomogeneous 3D domains using ten Tusscher's and Nygen's cardiac cell models. During propagation of depolarization wave, the(More)
In this paper, we propose the incorporation of artificial neural network (ANN) based supervised and unsupervised machine learning techniques into the evolutionary search, in order to detect strongly connected variables. The cost of extending a search method with an ANN based learning skill is relatively low, the memory requirements and model building cost(More)
Current multivariate EDAs rely on computationally efficient pairwise linkage detection mechanisms to identify higher order linkage blocks. Historical attempts to exemplify the potential disadvantage of this computational shortcut were scarcely successful. In this paper we introduce a new class of test functions to exemplify the inevitable weakness of the(More)
A major challenge in the field of metaheuristics is to find ways to increase the size of problems that can be addressed reliably. Scalability of probabilistic model building methods, capable to rendering difficult, large problems feasible by identifying dependencies, have been previously explored but investigations had mainly concerned problems where(More)