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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)
This paper presents a theoretical study of the behaviour of the Univari-ate Marginal Distribution Algorithm for continuous domains (UMDAc) in dimension n. To this end, the algorithm with tournament selection is modelled mathematically, assuming an infinite number of tournaments. The mathematical model is then used to study the algorithm's behaviour in the(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)
The application of the Bayesian Structural EM algorithm to learn Bayesian networks for clustering implies a search over the space of Bayesian network structures alternating between two steps: an optimization of the Bayesian network parameters (usually by means of the EM algorithm) and a structural search for model selection. In this paper, we propose to(More)
Estimation of distribution algorithms (EDAs) that use marginal product model factorizations have been widely applied to a broad range of mainly binary optimization problems. In this paper, we introduce the affinity propagation EDA (AffEDA) which learns a marginal product model by clustering a matrix of mutual information learned from the data using a very(More)
Many optimization problems are what can be called globally multimodal, i.e., they present several global optima. Unfortunately, this is a major source of difficulties for most estimation of distribution algorithms, making their effectiveness and efficiency degrade, due to genetic drift. With the aim of overcoming these drawbacks for discrete globally(More)