Social Media, Gender and the Mediatisation of War: Exploring the German Armed Forces’ Visual Representation of the Afghanistan Operation on Facebook

Studies on the mediatisation of war point to attempts of governments to regulate the visual perspective of their involvements in armed conflict – the most notable example being the practice of ‘embedded reporting’ in Iraq and Afghanistan. This paper focuses on a different strategy of visual meaning-making, namely, the publication of images on social media by armed forces themselves. Specifically, we argue that the mediatisation of war literature could profit from an increased engagement with feminist research, both within Critical Security/Critical Military Studies and within Science and Technology Studies that highlight the close connection between masculinity, technology and control. The article examines the German military mission in Afghanistan as represented on the German armed forces’ official Facebook page. Germany constitutes an interesting, and largely neglected, case for the growing literature on the mediatisation of war: its strong antimilitarist political culture makes the representation of war particularly delicate. The paper examines specific representational patterns of Germany’s involvement in Afghanistan and discusses the implications which arise from what is placed inside the frame of visibility and what remains out of its view

bDepartment of Business Administration,

One of the application areas of genetic algorithms is parameter optimization. This paper addresses the problem of optimizing a set of parameters that represent the weights of criteria, where the sum of all weights is 1. A chromosome represents the values of the weights, possibly along with some cut-off points. A new crossover operation, called continuous uniform crossover, is proposed, such that it produces valid chromosomes given that the parent chromosomes are valid. The new crossover technique is applied to the problem of multicriteria inventory classification. The results are compared with the classical inventory classification technique using Analytical Hierarchy Process

Abstract Curve fitting for styling application by

The fitting of curves in computer aided geometric design is generally regarded as an optimisation problem. According to the application, conditions to be satisfied can make the problem difficult to solve with classical methods. Stochastic ones, such as genetic algorithms, therefore appear as a relevant way of solving. We consider here a curve fitting problem aiming the generation of shapes with specific curvature variations. A particular curve model have been developed with this aim. Its implementation within a genetic algorithm have been carried out. We describe its main characteristics and present a first result. 1

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This paper presents a new methodology for continual improvement of cutting conditions with GA (Genetic Algorithms). It performs the following: the modification of recommended cutting conditions obtained from a machining data, learning of obtained cutting conditions using neural networks and the substitution of better cutting conditions for those learned previously by a proposed GA. Operators usually select the machining parameters according to handbooks or their experience, and the selected machining parameters are usually conservative to avoid machining failure. Compared to traditional optimisation methods, a GA is robust, global and may be applied generally without recourse to domain-specific heuristics. Experimental results show that the proposed genetic algorithm-based procedure for solving the optimization problem is both effective and efficient, and can be integrated into an intelligent manufacturing system for solving complex machining optimization problem

Decision making in an Adaptive Reservoir

It is now common knowledge that blind search algorithms cannot perform with equal e#ciency on all possible optimization problems defined on a domain. This knowledge applies also to Genetic Algorithms when viewed as global and blind optimizers. From this point of view it is necessary to design algorithms capable of adapting their search behaviour by making use in a direct fashion of the knowledge pertaining to the search landscape. The paper introduces a novel adaptive Genetic Algorithm where the exploration/exploitation balance is directly controlled using a Bayesian decision process. Test cases are analyzed as to how parameters a#ect the search behaviour of the algorithm

Multi-objective Co-operative Co-evolutionary

This paper presents the integration between two types of genetic algorithm: a multi-objective genetic algorithm (MOGA) and a co-operative co-evolutionary genetic algorithm (CCGA). The resulting algorithm is referred to as a multi-objective co-operative co-evolutionary genetic algorithm or MOCCGA. The integration between the twoalgorithms is carried out in order to improve the performance of the MOGA by adding the co-operativeco-evolutionary effect to the searchmechanisms employed by the MOGA. The MOCCGA is benchmarked against the MOGA in six different test cases. The test problems cover six differentcharacteristics that can be found within multi-objective optimisation problems: convex Pareto front, non-convex Pareto front, discrete Pareto front, multi-modality, deceptivePareto front and non-uniformity in the solution distribution. The simulation results indicate that overall the MOCCGA is superior to the MOGA in terms of the variety in solutions generated and the closeness of solutions to the true Pareto-optimal solutions