Titlebook: Leading in the NHS; A Practical Guide Rosemary Stewart Textbook 1989Latest edition Macmillan Publishers Limited 1989 amyotrophic lateral sc

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Rosemary Stewart computing the gradient of a set of points with respect to its S-metric on basis of the gradients of the objective functions. It discusses singularities where the gradient is vanishing or differentiability is one sided. To circumvent the problem of vanishing gradient components of the S-metric for d

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Rosemary Stewart in their domain. The book is intended both to provide an overview of hybrid metaheuristics to novices of the field, and to provide researchers from the field with a collection of some of the most interesting recent developments..978-3-642-09697-6978-3-540-78295-7Series ISSN 1860-949X Series E-ISSN 1860-9503

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Rosemary Stewartthe asymmetric aspects in a VRPTW. We use Tabu Search for the generation of the ejection chains, and on a higher algorithmic level, the ejection chain process is embedded into an Iterated Local Search algorithm. Computational results confirm that this approach leads to very fast algorithms, showing

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Rosemary Stewartthe asymmetric aspects in a VRPTW. We use Tabu Search for the generation of the ejection chains, and on a higher algorithmic level, the ejection chain process is embedded into an Iterated Local Search algorithm. Computational results confirm that this approach leads to very fast algorithms, showing

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Rosemary Stewartood, it ends to a neighborhood that includes all the swarm, it manages to take advantage of the exploration capabilities of a global neighborhood structure and of the exploitation abilities of a local neighborhood structure. In order to test the effectiveness and the efficiency of the proposed metho

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Rosemary Stewart?cial intelligence solving methods. Most of these papers are not only a proof of concept – which can be valuable by itself – but also show that the hybrid techniques presented tackle di?cult and relevant problems. In keeping with the tradition of this workshop, special care was exercised in the revi

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Rosemary Stewartessed. We compare our algorithm to state of the art results on a set of existing real life and randomly generated instances. Our new algorithm returns improved results on 28 out of the 30 benchmark instances. To the best of our knowledge, these results constitute current upper bounds for the respect