Titlebook: High Performance Computing in Science and Engineering ‘ 06; Transactions of the Wolfgang E. Nagel,Willi Jäger,Michael Resch Conference pro

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W. G. Schmidt,S. Blankenburg,S. Wippermann,A. Hermann,P. H. Hahn,M. Preuss,K. Seino,F. Bechstedthe optimization can represent an issue for the practical use. However, by tuning the parameter setting of the employed solver, it is possible to speed up the optimization process. The present work evaluates two popular hyperparameter tuning tools – irace (iterated racing) and SMAC (sequential model-

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S. Ganzenmüller,A. Nagel,S. Holtwick,W. Rosenstiel,H. Ruderive process. These algorithms are designed expecting a good balance between exploration and exploitation during the search process. Besides, the diversity of the population is crucial to properly explore the search space. This article introduces an improved version of the Differential Evolution (DE)

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nized that one of the major obstacles in addressing this question is that the “standard” computational approaches are not powerful enough to search for the correct structure in the huge conformational space. Genetic algorithms, a cooperative computational method, have been successful in many difficu

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P. Henseler,C. Schieback,K. Franzrahe,F. Bürzle,M. Dreher,J. Neder,W. Quester,M. Kläui,U. Rüdiger,P.nized that one of the major obstacles in addressing this question is that the “standard” computational approaches are not powerful enough to search for the correct structure in the huge conformational space. Genetic algorithms, a cooperative computational method, have been successful in many difficu

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Peter Schmitteckert,Günter Schneiderevoted a tremendous amount of research to find an optimal circle detector. On the other hand, Evolutionary Algorithms (EA) are earning popularity as computational intelligence approaches for solving complex problems that are encountered in many engineering disciplines. They have exhibited robustness

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Erik Bitzek,Peter Gumbsch the way in which animals behave collectively assuming the overall detection process as a multi-modal optimization problem. In the algorithm, searcher agents emulate a group of animals that interact to each other using simple biological rules which are modeled as evolutionary operators. In turn, suc

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C. Lavalle,S. R. Manmana,S. Wessel,A. Muramatsu this topic contains a lot of heuristics. These heuristics, however, have limited applicability because they have a number of fundamental problems including high time complexity, and lack of scalability with respect to optimal solutions. We propose a hybrid genetic algorithm/heuristic based algorith