The potential of evolutionary algorithms in multiobjective optimization was identified early in their history. That potential has been realized over the years with the development of increasingly elaborate Evolutionary Multiobjective Optimization (EMO) algorithms that have found many important applications in the real world, and have contributed significantly to the growth in popularity of multiobjective optimization in general.
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Real world complex systems can be modeled as networks of linked elements called complex networks. The topology and dynamics of such networks have been shown more similar than expected, independently of their domain or function -- e.g., sociology, biology and web science -- enabling the generalization of models and reuse of solutions, fostering a convergence of several domains to a common paradigm around a network science.
The ever-increasing number of gadgets being used to create digital content, as well as the easiness in sharing, editing, and republishing this content, brings the problem of dealing with a large amount of digital objects (e.g., images or videos) whose content is very similar. Some issues faced by investigators of digital crimes when analysing this type of data include finding the original source of a suspect image, for instance, and the responsible for first publishing it. It is also challenging to determine how a set of similar objects are related to each other.
A evolução do processo de fabricação de semicondutores para escala nanom étrica traz, juntamente com benefícios no consumo de energia e área, controle reduzido sobre parâmetros críticos dos dispositivos. Sistemas de computação contemporâneos vêm apresentando variações significantes – especialmente no consumo de energia – entre unidades nominalmente idênticas, ao longo da vida de cada dispositivo, e como consequência de diferentes condições ambientais.