2 research outputs found

    Evolution strategies for robust optimization

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    Real-world (black-box) optimization problems often involve various types of uncertainties and noise emerging in different parts of the optimization problem. When this is not accounted for, optimization may fail or may yield solutions that are optimal in the classical strict notion of optimality, but fail in practice. Robust optimization is the practice of optimization that actively accounts for uncertainties and/or noise. Evolutionary Algorithms form a class of optimization algorithms that use the principle of evolution to find good solutions to optimization problems. Because uncertainty and noise are indispensable parts of nature, this class of optimization algorithms seems to be a logical choice for robust optimization scenarios. This thesis provides a clear definition of the term robust optimization and a comparison and practical guidelines on how Evolution Strategies, a subclass of Evolutionary Algorithms for real-parameter optimization problems, should be adapted for such scenarios.UBL - phd migration 201

    Scheduling (Dagstuhl Seminar 18101)

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    This report documents the program and outcomes of the Dagstuhl Seminar 18101 "Scheduling" in March 2018. The seminar brought together algorithmically oriented researchers from two communities with interests in resource management: (i) the scheduling community and (ii) the networking and distributed computing community. The primary objective of the seminar was to expose each community to the important problems and techniques from the other community, and to facilitate dialog and collaboration between researchers
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