Abstract
The environmental concerns are having a significant impact on the operation of power systems. The traditional Unit Commitment problem, which to minimizes the fuel cost is inadequate when environmental emissions are also considered in the operation of power plants. This paper presents a Biased Random Key Genetic Algorithm (BRKGA) approach combined with non-dominated sorting procedure to find solutions for the unit commitment multiobjective optimization problem. In the first stage, the BRKGA solutions are encoded by using random keys, which are represented as vectors of real numbers in the interval [0,1]. In the subsequent stage, a non-dominated sorting procedure similar to NSGA II is employed to approximate the set of Pareto solution through an evolutionary optimization process. 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. Test results with the existent benchmark systems of 10 units and 24 hours scheduling horizon are presented. The comparison of the obtained results with those of other Unit Commitment (UC) multiobjective optimization methods reveal the effectiveness of the proposed method.

Abstract
In this work we propose a multi-population genetic algorithm for tree-shaped network design problems using random keys. Recent literature on finding optimal spanning trees suggests the use of genetic algorithms. Furthermore, random keys encoding has been proved efficient at dealing with problems where the relative order of tasks is important. Here we propose to use random keys for encoding trees. The topology of these trees is restricted, since no path from the root vertex to any other vertex may have more than a pre-defined number of arcs. In addition, the problems under consideration also exhibit the characteristic of flows. Therefore, we want to find a minimum cost tree satisfying all demand vertices and the pre-defined number of arcs. The contributions of this paper are twofold: on one hand we address a new problem, which is an extension of the well known NP-hard hop-constrained MST problem since we also consider determining arc flows such that vertices requirements are met at minimum cost and the cost functions considered include a fixed cost component and a nonlinear flow routing component; on the other hand, we propose a new genetic algorithm to efficiently find solutions to this problem.

Abstract
In this article we address the problem of determining how a structured formation of autonomous undistinguishable agents can be reorganized into another, eventually non-rigid, formation based on changes in the environment, perhaps unforeseeable. The methodology can also be used to correctly position the agents into a particular formation from an initial set of random locations. Given the information on the agents current location and the final locations, there are n! possible permutations for the n agents. Among these, we seek one that minimizes a total relative measure, e.g. distance traveled by the agents during the switching. We propose a dynamic programming methodology to solve this problem to optimality. Possible applications can be found in surveillance, damage assessment, chemical or biological monitoring, among others, where the switching to another formation, not necessarily predefined, may be required due to changes in the environment.

Abstract
We propose a dynamic programming approach to address the problem of determining how a structured formation of autonomous undistinguishable agents can be reorganized into another, eventually non-rigid, formation based on changes in the environment, perhaps unforeseeable. The methodology can also be used to correctly position the agents into a particular formation from an initial set of random locations. Given the information of the current agents location and the final locations, there are n! possible permutations for the n agents, and we seek the one that minimizes a total relative measure, e.g. distance traveled by the agents during the switching. Possible applications can be found amongst surveillance, damage assessment, chemical or biological monitoring, among others. where the switching to another formation, not necessarily predefined, may be required due to changes in the environment.

Abstract
Energy policies in the European Union (EU) and its 27 member states respond to three main concerns namely energy security, economic development, and environmental sustainability. All the three "Es'' are pursued simultaneously with some slight differences in emphasizing the mutual importance of these, in particular the cost factors. The legislation of the EU (e. g., ETS-Emission Trading Scheme, directives) increasingly guides the member states' energy policies. However, energy policy directions are still made domestically, for example, on the support on renewable energy technologies. In this work, we look into distributed generation (DG), since it has been grown considerable in the past few years and can be used to partially fulfill renewable energy targets. The policy makers have to make decisions about regulation directives, more specifically they have to change the current regulation in order to incentive the increase in DG. However, these decisions have not only economic impacts but also technical impacts that must be accounted for. In this regard, a decision aid tool would help the policy makers in estimating producer economic impacts, as well as power network technical impacts, of various possible regulation directives. Here, we propose an interactive decision aid tool that models the aforementioned impacts and thus, can be used by policy makers to experiment with different regulation directives before deciding on the ones to set.

Abstract