José Antonio Lozano

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Cloud computing environments allow customers to dynamically scale their applications. The key problem is how to lease the right amount of resources, on a pay-as-you-go basis. Application re-dimensioning can be implemented effortlessly, adapting the resources assigned to the application to the incoming user demand. However, the identification of the right(More)
In this paper, we aim to compare empirically four initialization methods for the K-Means algorithm: random, Forgy, MacQueen and Kaufman. Although this algorithm is known for its robustness, it is widely reported in literature that its performance depends upon two key points: initial clustering and instance order. We conduct a series of experiments to draw(More)
Simplified lattice models have played an important role in protein structure prediction and protein folding problems. These models can be useful for an initial approximation of the protein structure, and for the investigation of the dynamics that govern the protein folding process. Estimation of distribution algorithms (EDAs) are efficient evolutionary(More)
Differences in gene expression patterns have been documented not only in Multiple Sclerosis patients versus healthy controls but also in the relapse of the disease. Recently a new gene expression modulator has been identified: the microRNA or miRNA. The aim of this work is to analyze the possible role of miRNAs in multiple sclerosis, focusing on the relapse(More)
One of the main tasks software testing includes is the generation of the test cases to be used during the test. Due to its expensive cost, the automatization of this task has become one of the key issues in the area. The field of Evolutionary Testing deals with this problem by means of metaheuristic search techniques. An Evolutionary Testing based approach(More)
This article reviews machine learning methods for bioinformatics. It presents modelling methods, such as supervised classification, clustering and probabilistic graphical models for knowledge discovery, as well as deterministic and stochastic heuristics for optimization. Applications in genomics, proteomics, systems biology, evolution and text mining are(More)
The portfolio optimization problem uses mathematical approaches to model stock exchange investments. Its aim is to find an optimal set of assets to invest on, as well as the optimal investments for each asset. In the present work, the problem is treated as a multiobjective optimization problem. Three well-known optimization techniques greedy search,(More)
Estimation of distribution algorithms (EDAs) are a set of algorithms that belong to the field of Evolutionary Computation. Characterized by the use of probabilistic models to represent the solutions and the dependencies between the variables of the problem, these algorithms have been applied to a wide set of academic and real-world optimization problems,(More)
Multi-dimensional classification is a generalization of supervised classification that considers more than one class variable to classify. In this paper we review the existing multi-dimensional Bayesian classifiers and introduce a new one: the KDB multi-dimensional classifier. Then we define different classification rules for multi-dimensional scope.(More)