Pinus uncinata Ramond ex DC.: Conservation and proposal of management at its southwestern limit of distribution (Castillo de Vinuesa, Soria, Spain)

Pinus uncinata forms forests in the centre and southwest of the Alps and in the subalpine Pyrenees (at around 1700 – 2600 m) (Costa Tenorio et al., 1997). The species reaches the southwestern limit of its distribution at the top of Mount Castillo de Vinuesa (Soria, Spain). The small population on this mountain occupies just 66 ha, but is very important from a geobotanical viewpoint since it is just one of two populations (the other being in the Sierra de Gúdar range in Teruel, Spain) isolated from the main area where the species is found in the Iberian Peninsula (The Pyrenees)

A review of estimation of distribution algorithms in bioinformatics

Evolutionary search algorithms have become an essential asset in the algorithmic toolbox for solving high-dimensional optimization problems in across a broad range of bioinformatics problems. Genetic algorithms, the most well-known and representative evolutionary search technique, have been the subject of the major part of such applications. Estimation of distribution algorithms (EDAs) offer a novel evolutionary paradigm that constitutes a natural and attractive alternative to genetic algorithms. They make use of a probabilistic model, learnt from the promising solutions, to guide the search process. In this paper, we set out a basic taxonomy of EDA techniques, underlining the nature and complexity of the probabilistic model of each EDA variant. We review a set of innovative works that make use of EDA techniques to solve challenging bioinformatics problems, emphasizing the EDA paradigm's potential for further research in this domain.

Multi-objective Estimation of Distribution Algorithm Based on Joint Modeling of Objectives and Variables

This paper proposes a new multi-objective estimation of distribution algorithm (EDA) based on joint modeling of objectives and variables. This EDA uses the multi-dimensional Bayesian network as its probabilistic model. In this way it can capture the dependencies between objectives, variables and objectives, as well as the dependencies learnt between variables in other Bayesian network-based EDAs. This model leads to a problem decomposition that helps the proposed algorithm to find better trade-off solutions to the multi-objective problem. In addition to Pareto set approximation, the algorithm is also able to estimate the structure of the multi-objective problem. To apply the algorithm to many-objective problems, the algorithm includes four different ranking methods proposed in the literature for this purpose. The algorithm is applied to the set of walking fish group (WFG) problems, and its optimization performance is compared with an evolutionary algorithm and another multi-objective EDA. The experimental results show that the proposed algorithm performs significantly better on many of the problems and for different objective space dimensions, and achieves comparable results on some compared with the other algorithms.

Origin and patterns of distribution of trace elements in street dust. Unleaded petrol and urban lead

The elemental composition, patterns of distribution and possible sources of street dust are not common to all urban environments, but vary according to the peculiarities of each city. The common features and dissimilarities in the origin and nature of street dust were investigated through a series of studies in two widely different cities, Madrid (Spain) and Oslo (Norway), between 1990 and 1994. The most comprehensive sampling campaign was carried out in the Norwegian capital during the summer of 1994. An area of 14 km2, covering most of downtown Oslo and some residential districts to the north of the city, was divided into 1 km2 mapping units, and 16 sampling increments of approximately 150 g were collected from streets and roads in each of them. The fraction below 100 μm was acid-digested and analysed by ICP-MS. Statistical analyses of the results suggest that chemical elements in street dust can be classified into three groups: “urban” elements (Ba, Cd, Co, Cu, Mg, Pb, Sb, Ti, Zn), “natural” elements (Al, Ga, La, Mn, Na, Sr, Th, Y) and elements of a mixed origin or which have undergone geochemical changes from their original sources (Ca, Cs, Fe, Mo, Ni, Rb, Sr, U). Soil resuspension and/or mobilisation appears to be the most important source of “natural” elements, while “urban” elements originate primarily from traffic and from the weathering and corrosion of building materials. The data for Pb seem to prove that the gradual shift from leaded to unleaded petrol as fuel for automobiles has resulted in an almost proportional reduction in the concentration of Pb in dust particles under 100 μm. This fact and the spatial distribution of Pb in the city strongly suggest that lead sources other than traffic (i.e. lead accumulated in urban soil over the years) may contribute as much lead, if not more, to urban street dust.

Distributed Estimation of Distribution Algorithms for continuous optimization: how does the exchanged information influence their behavior?

One of the most promising areas in which probabilistic graphical models have shown an incipient activity is the field of heuristic optimization and, in particular, in Estimation of Distribution Algorithms. Due to their inherent parallelism, different research lines have been studied trying to improve Estimation of Distribution Algorithms from the point of view of execution time and/or accuracy. Among these proposals, we focus on the so-called distributed or island-based models. This approach defines several islands (algorithms instances) running independently and exchanging information with a given frequency. The information sent by the islands can be either a set of individuals or a probabilistic model. This paper presents a comparative study for a distributed univariate Estimation of Distribution Algorithm and a multivariate version, paying special attention to the comparison of two alternative methods for exchanging information, over a wide set of parameters and problems ? the standard benchmark developed for the IEEE Workshop on Evolutionary Algorithms and other Metaheuristics for Continuous Optimization Problems of the ISDA 2009 Conference. Several analyses from different points of view have been conducted to analyze both the influence of the parameters and the relationships between them including a characterization of the configurations according to their behavior on the proposed benchmark.

An Interval-based Multiobjective Approach to Feature Subset Selection Using Joint Modeling of Objectives and Variables

This paper studies feature subset selection in classification using a multiobjective estimation of distribution algorithm. We consider six functions, namely area under ROC curve, sensitivity, specificity, precision, F1 measure and Brier score, for evaluation of feature subsets and as the objectives of the problem. One of the characteristics of these objective functions is the existence of noise in their values that should be appropriately handled during optimization. Our proposed algorithm consists of two major techniques which are specially designed for the feature subset selection problem. The first one is a solution ranking method based on interval values to handle the noise in the objectives of this problem. The second one is a model estimation method for learning a joint probabilistic model of objectives and variables which is used to generate new solutions and advance through the search space. To simplify model estimation, l1 regularized regression is used to select a subset of problem variables before model learning. The proposed algorithm is compared with a well-known ranking method for interval-valued objectives and a standard multiobjective genetic algorithm. Particularly, the effects of the two new techniques are experimentally investigated. The experimental results show that the proposed algorithm is able to obtain comparable or better performance on the tested datasets.