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A novel adaptive density-based ACO algorithm with minimal encoding redundancy for clustering problems
(2016 IEEE Congress on Evolutionary Computation, CEC 2016, 2016-11-14)
In the so-called Big Data paradigm descriptive analytics are widely conceived as techniques and models aimed at discovering knowledge within unlabeled datasets (e.g. patterns, similarities, etc) of utmost help for subsequent ...
An optimization tool to design the field of a Solar Power Tower plant allowing heliostats of different sizes
(International Journal of Energy Research, 2016-10-30)
The design of a Solar Power Tower plant involves the optimization of the heliostat field layout. Fields are usually designed to have all heliostats of identical size. Although the use of a single heliostat size has been ...
Estimating attraction basin sizes
(Lecture Notes in Computer Science, 2016-10-01)
The performance of local search algorithms is influenced by the properties that the neighborhood imposes on the search space. Among these properties, the number of local optima has been traditionally considered as a ...
A note on the Boltzmann distribution and the linear ordering problem
(Lecture Notes in Computer Science, 2016-10-01)
The Boltzmann distribution plays a key role in the field of optimization as it directly connects this field with that of probability. Basically, given a function to optimize, the Boltzmann distribution associated to this ...
Improved Quantification of Important Beer Quality Parameters based on Non-linear Calibration Methods applied to FT-MIR Spectra
(Analytical and Bioanalytical Chemistry, 2016-07-14)
During the production process of beer, it is of utmost importance to guarantee a high consistency of the beer quality. For instance, the bitterness is an essential quality parameter which has to be controlled within the ...
Efficient approximation of probability distributions with k-order decomposable models
(International Journal of Approximate Reasoning, 2016-07)
During the last decades several learning algorithms have been proposed to learn probability distributions based on decomposable models. Some of these algorithms can be used to search for a maximum likelihood decomposable ...
An efficient approximation to the K-means clustering for Massive Data
(Knowledge-Based Systems, 2016-06-28)
Due to the progressive growth of the amount of data available in a wide variety of scientific fields, it has become more difficult to manipulate and analyze such information. In spite of its dependency on the initial ...
Fitting the data from embryo implantation prediction: Learning from label proportions
(Statistical Methods in Medical Research, 2016-01-01)
Machine learning techniques have been previously used to assist clinicians to select embryos for human-assisted reproduction. This work aims to show how an appropriate modeling of the problem can contribute to improve ...
Efficient approximation of probability distributions with k-order decomposable models
(International Journal of Approximate Reasoning, 2016-01-01)
During the last decades several learning algorithms have been proposed to learn probability distributions based on decomposable models. Some of these algorithms can be used to search for a maximum likelihood decomposable ...
Joint topology optimization, power control and spectrum allocation for intra-vehicular multi-hop sensor networks using dandelion-encoded heuristics
(Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2016-01-01)
In the last years the interest in multi-hop communications has gained momentum within the research community due to the challenging characteristics of the intra-vehicular radio environment and the stringent robustness ...