Abstract
Collaborative Data Mining (CDM) develops techniques to solve complex problems of data analysis requiring sets of experts in different domains that may be geographically separate. An important issue in CDM is the sharing of experience among the different experts. In this paper we report on a framework that enables users with different expertise to perform data analysis activities and profit, in a collaborative fashion, from expertise and results of other researchers. The collaborative process is supported by web services that seek for relevant knowledge available among the collaborative web sites. We have successfully designed and deployed a prototype for collaborative Data Mining in domains of Molecular Biology and Chemoinformatics.

Abstract

Abstract
A Biased Random Key Genetic Algorithm (BRKGA) is proposed to find solutions for the unit commitment problem. In this problem, one wishes to schedule energy production on a given set of thermal generation units in order to meet energy demands at minimum cost, while satisfying a set of technological and spinning reserve constraints. In the BRKGA, solutions are encoded by using random keys, which are represented as vectors of real numbers in the interval [0, 1]. The GA proposed is a variant of the random key genetic algorithm, since bias is introduced in the parent selection procedure, as well as in the crossover strategy. Tests have been performed on benchmark large-scale power systems of up 100 units for a 24 hours period. The results obtained have shown the proposed methodology to be an effective and efficient tool for finding solutions to large-scale unit commitment problems. Furthermore, form the comparisons made it can be concluded that the results produced improve upon the best known solutions.

Abstract
We address the problem of dynamically switching the topology of a formation of a number of undistinguishable agents. Given the current and the final topologies, each with n agents, there are n! possible allocations between the initial and final positions of the agents. Given the agents maximum velocities, there is still a degree of freedom in the trajectories that might be used in order to avoid collisions. We seek an allocation and corresponding agent trajectories minimizing the maximum time required by all agents to reach the final topology, avoiding collisions. Collision avoidance is guaranteed through an appropriate choice of trajectories, which might have consequences in the choice of an optimal permutation. We propose here a dynamic programming approach to optimally solve problems of small dimension. We report computational results for problems involving formations with up to 12 agents. © Springer Science+Business Media, LLC 2010.

Abstract
In this work we propose a new problem, that we have named hop-constrained minimum cost flow spanning tree problem, and develop an exact solution methodology. This problem is an extension of the well know NP-hard hop-constrained Minimum Spanning Tree problem (MST), since in addition to finding the arcs to be used we also must find the amount of flow that is to be routed through each arc. The hop-constrained MST has numerous practical applications in the design of communication networks. The hop constraints are usually used to guarantee a certain quality of service with respect to availability, reliability and lower delays, since they limit the number of arcs in each path from the central service provider. Including the flows, as we propose, allows for different levels of service requirements. A further extension is considered: the cost functions may have any type or form, may be neither convex nor concave, and need not to be differentiable or continuous. We develop a dynamic programming approach, which extends the scope of application of a previous work, to solve to optimality such problems. Computational experiments are performed using randomly generated test problems. Results showing the robustness of the method are reported.

Abstract
We compute the analytic expression of the probability distributions F(AEX,+) and F(AEX,-) of the normalized positive and negative AEX (Netherlands) index daily returns r(t). Furthermore, we define the alpha re-scaled AEX daily index positive returns r(t)(alpha) and negative returns (-r(t))(alpha), which we call, after normalization, the alpha positive fluctuations and alpha negative fluctuations. We use the Kolmogorov-Smirnov statistical test as a method to find the values of alpha that optimize the data collapse of the histogram of the alpha fluctuations with the Bramwell-Holdsworth-Pinton (BHP) probability density function. The optimal parameters that we found are alpha(+) = 0.46 and alpha(-) = 0.43. Since the BHP probability density function appears in several other dissimilar phenomena, our result reveals a universal feature of stock exchange markets.