Abstract
A random-key genetic algorithm is an evolutionary metaheuristic for discrete and global optimization. Each solution is encoded as an array of n random keys, where a random key is a real number, randomly generated, in the continuous interval [0,1] A decoder maps each array of random keys to a solution of the optimization problem being solved and computes its cost. The algorithm starts with a population of p arrays of random keys. At each iteration, the arrays are partitioned into two sets, a smaller set of high-valued elite solutions and the remaining nonelite solutions. All elite elements are copied, without change, to the next population. A small number of random-key arrays (the mutants) are added to the population of the next iteration. The remaining elements of the population of the next iteration are generated by combining, with the parametrized uniform crossover of Spears and DeJong (On the virtues of parameterized uniform crossover. In: Proceedings of the fourth international conference on genetic algorithms, San Mateo, pp 230-236, 1991), pairs of arrays. This chapter reviews random-key genetic algorithms and describes an effective variant called biased random-key genetic algorithms.

Abstract
With the rapid growth in the use of digital platforms for the dissemination and expansion of a company’s business reach, it is vitally important that startup firms are firmly aware of the options when deciding whether to adopt a particular technology because they often have low resource availability, which reduces their margin for error. In order to help these companies to adopt Digital Marketing in a more secure way by knowing the most relevant factors that they can find as concerns the adoption of technologies, this study will analyze the factors that influence the adoption of technology, identifying them initially through the systematic literature review of similar scientific works. © Springer International Publishing AG 2018.

Abstract
The environmental impact and the scarcity of limited fossil fuels led to the need of investment in energy based on renewable sources. This has driven Europe to implement several policies that changed the energy market's paradigm, namely the incentive to microgeneration. The penetration of energy sources from intermittent nature has increased the unpredictability of the system, which makes simulation and analysis tools essential in order to provide decision support to entities in this sector. This paper presents the Tools Control Center (TOOCC) as a solution to increase the interoperability between heterogeneous agent-based systems, in the energy field. The proposed approach acts as a facilitator in the interaction between different systems through the usage of ontologies, allowing them to communicate in the same language. To understand the real applicability of this tool, a case study is presented concerning the interaction between several systems, with the purpose of enabling the energy resource scheduling of a microgrid, and the reaction of a house managed by a house management system.

Abstract
Background Changes in the retinal vessel caliber are associated with a variety of major diseases, namely diabetes, hypertension and atherosclerosis. The clinical assessment of these changes in fundus images is tiresome and prone to errors and thus automatic methods are desirable for objective and precise caliber measurement. However, the variability of blood vessel appearance, image quality and resolution make the development of these tools a non-trivial task. Metholodogy A method for the estimation of vessel caliber in eye fundus images via vessel cross-sectional intensity profile model fitting is herein proposed. First, the vessel centerlines are determined and individual segments are extracted and smoothed by spline approximation. Then, the corresponding cross-sectional intensity profiles are determined, post-processed and ultimately fitted by newly proposed parametric models. These models are based on Difference-of-Gaussians (DoG) curves modified through a multiplying line with varying inclination. With this, the proposed models can describe profile asymmetry, allowing a good adjustment to the most difficult profiles, namely those showing central light reflex. Finally, the parameters of the best-fit model are used to determine the vessel width using ensembles of bagged regression trees with random feature selection. Results and conclusions The performance of our approach is evaluated on the REVIEW public dataset by comparing the vessel cross-sectional profile fitting of the proposed modified DoG models with 7 and 8 parameters against a Hermite model with 6 parameters. Results on different goodness of fitness metrics indicate that our models are constantly better at fitting the vessel profiles. Furthermore, our width measurement algorithm achieves a precision close to the observers, outperforming state-of-the art methods, and retrieving the highest precision when evaluated using cross-validation. This high performance supports the robustness of the algorithm and validates its use in retinal vessel width measurement and possible integration in a system for retinal vasculature assessment.

Abstract
This paper presents a new framework for optimizing the operation of Industrial MicroGrids (IMG). The proposed framework consists of three levels. At the first level, a Profit Based Security Constrained Unit Commitment (PB-SCUC) is solved in order to minimize the total expected cost of IMG via maximizing the IMG revenue by transacting in the day-ahead power market and optimizing the scheduling of the units. In this paper, the tendency of IMG for participating in the day-ahead power market is modelled as a quadric function. At the second level, a Security Constrained Unit Commitment is solved at the upper grid for minimizing the upper grid operation and guaranteeing its security. At this level, the accepted IMG bids in the day-ahead power market would be determined. Finally, at the third level, the IMG operator must settle its units on the basis of its accepted bids. Therefore, a rescheduling problem is solved in the third level. Notably, Renewable Energy Sources (RESs), Combined Heat and Power (CHP) units, thermal and electrical storage systems are considered in the IMG. As the RESs and day-ahead market price have stochastic behaviours, their uncertainty is taken into account by implementing stochastic programming. Further, different cases for grid-connected and island modes of IMG are discussed, and the advantages of utilizing RES and storage systems are given. The simulation results are provided based on the IEEE 18-bus test system for IMG and IEEE 30-bus test system for the upper grid.

Abstract
This paper presents the Electrum Analyzer, a free-software tool to validate and perform model checking of Electrum specifications. Electrum is an extension of Alloy that enriches its relational logic with LTL operators, thus simplifying the specification of dynamic systems. The Analyzer supports both automatic bounded model checking, with an encoding into SAT, and unbounded model checking, with an encoding into SMV. Instance, or counter-example, traces are presented back to the user in a unified visualizer. Features to speed up model checking are offered, including a decomposed parallel solving strategy and the extraction of symbolic bounds. Source code: https://github.com/haslab/ElectrumVideo: https://youtu.be/FbjlpvjgMDA.