Technical University of Dortmund

About: Technical University of Dortmund is a education organization based out in Dortmund, Nordrhein-Westfalen, Germany. It is known for research contribution in the topics: Context (language use) & Large Hadron Collider. The organization has 13028 authors who have published 27666 publications receiving 615557 citations. The organization is also known as: Dortmund University & University of Dortmund.

De novo branching cascades for structural and functional diversity in small molecules

Abstract: Generating diverse structures with a minimum amount of synthetic effort is an important goal for drug discovery. Here, the authors report a two-phase synthesis for the generation of skeletally diverse small molecules—forming molecular scaffolds and subsequently diversifying each into multiple structures.

On the Choice of the Offspring Population Size in Evolutionary Algorithms

Abstract: Evolutionary algorithms (EAs) generally come with a large number of parameters that have to be set before the algorithm can be used. Finding appropriate settings is a difficult task. The influence of these parameters on the efficiency of the search performed by an evolutionary algorithm can be very high. But there is still a lack of theoretically justified guidelines to help the practitioner find good values for these parameters. One such parameter is the offspring population size. Using a simplified but still realistic evolutionary algorithm, a thorough analysis of the effects of the offspring population size is presented. The result is a much better understanding of the role of offspring population size in an EA and suggests a simple way to dynamically adapt this parameter when necessary.

The contribution of fermi-2lac blazars to diffuse tev-pev neutrino flux

Abstract: M. G. Aartsen, K. Abraham, M. Ackermann, J. Adams, J. A. Aguilar, M. Ahlers, M. Ahrens, D. Altmann, K. Andeen, T. Anderson, I. Ansseau, G. Anton, M. Archinger, C. Arguelles, T. C. Arlen, J. Auffenberg, S. Axani, X. Bai, S. W. Barwick, V. Baum, R. Bay, J. J. Beatty, J. Becker Tjus, K.-H. Becker, S. BenZvi, P. Berghaus, D. Berley, E. Bernardini, A. Bernhard, D. Z. Besson, G. Binder, D. Bindig, M. Bissok, E. Blaufuss, S. Blot, D. J. Boersma, C. Bohm, M. Borner, F. Bos, D. Bose, S. Boser, O. Botner, J. Braun, L. Brayeur, H.-P. Bretz, A. Burgman, J. Casey, M. Casier, E. Cheung, D. Chirkin, A. Christov, K. Clark, L. Classen, S. Coenders, G. H. Collin, J. M. Conrad, D. F. Cowen, A. H. Cruz Silva, J. Daughhetee, J. C. Davis, M. Day, J. P. A. M. de Andre, C. De Clercq, E. del Pino Rosendo, H. Dembinski, S. De Ridder, P. Desiati, K. D. de Vries, G. de Wasseige, M. de With, T. DeYoung, J. C. Diaz-Velez, V. di Lorenzo, H. Dujmovic, J. P. Dumm, M. Dunkman, B. Eberhardt, T. Ehrhardt, B. Eichmann, S. Euler, P. A. Evenson, S. Fahey, A. R. Fazely, J. Feintzeig, J. Felde, K. Filimonov, C. Finley, S. Flis, C.-C. Fosig, A. Franckowiak, T. Fuchs, T. K. Gaisser, R. Gaior, J. Gallagher, L. Gerhardt, K. Ghorbani, W. Giang, L. Gladstone, M. Glagla, T. Glusenkamp, A. Goldschmidt, G. Golup, J. G. Gonzalez, D. GOra, D. Grant, Z. Griffith, C. Haack, A. Haj Ismail, A. Hallgren, F. Halzen, E. Hansen, B. Hansmann, T. Hansmann, K. Hanson, D. Hebecker, D. Heereman, K. Helbing, R. Hellauer, S. Hickford, J. Hignight, G. C. Hill, K. D. Hoffman, R. Hoffmann, K. Holzapfel, A. Homeier, K. Hoshina, F. Huang, M. Huber, W. Huelsnitz, K. Hultqvist, S. In, A. Ishihara, E. Jacobi, G. S. Japaridze, M. Jeong, K. Jero, B. J. P. Jones, M. Jurkovic, A. Kappes, T. Karg, A. Karle, U. Katz, M. Kauer, A. Keivani, J. L. Kelley, J. Kemp, A. Kheirandish, M. Kim, T. Kintscher, J. Kiryluk, T. Kittler, S. R. Klein, G. Kohnen, R. Koirala, H. Kolanoski, R. Konietz, L. Kopke, C. Kopper, S. Kopper, D. J. Koskinen, M. Kowalski, K. Krings, M. Kroll, G. Kruckl, C. Kruger, J. Kunnen, S. Kunwar, N. Kurahashi, T. Kuwabara, M. Labare, J. L. Lanfranchi, M. J. Larson, D. Lennarz, M. Lesiak-Bzdak, M. Leuermann, J. Leuner, L. Lu, J. Lunemann, J. Madsen, G. Maggi, K. B. M. Mahn, S. Mancina, M. Mandelartz, R. Maruyama, K. Mase, R. Maunu, F. McNally, K. Meagher, M. Medici, M. Meier, A. Meli, T. Menne, G. Merino, T. Meures, S. Miarecki, E. Middell, L. Mohrmann, T. Montaruli, M. Moulai, R. Nahnhauer, U. Naumann, G. Neer, H. Niederhausen, S. C. Nowicki, D. R. Nygren, A. Obertacke Pollmann, A. Olivas, A. Omairat, A. O, Murchadha, T. Palczewski, H. Pandya, D. V. Pankova, O. Penek, J. A. Pepper, C. Perez de los Heros, C. Pfendner, D. Pieloth, E. Pinat, J. Posselt, P. B. Price, G. T. Przybylski, M. Quinnan, C. Raab, L. Radel, M. Rameez, K. Rawlins, R. Reimann, M. Relich, E. Resconi, W. Rhode, M. Richman, B. Riedel, S. Robertson, M. Rongen, C. Rott, T. Ruhe, D. Ryckbosch, D. Rysewyk, L. Sabbatini, S. E. Sanchez Herrera, A. Sandrock, J. Sandroos, S. Sarkar, K. Satalecka, M. Schimp, P. Schlunder, T. Schmidt, S. Schoenen, S. Schoneberg, A. Schonwald, L. Schumacher, D. Seckel, S. Seunarine, D. Soldin, M. Song, G. M. Spiczak, C. Spiering, M. Stahlberg, M. Stamatikos, T. Stanev, A. Stasik, A. Steuer, T. Stezelberger, R. G. Stokstad, A. Stosl, R. Strom, N. L. Strotjohann, G. W. Sullivan, M. Sutherland, H. Taavola, I. Taboada, J. Tatar, S. Ter-Antonyan, A. Terliuk, G. TesiC, S. Tilav, P. A. Toale, M. N. Tobin, S. Toscano, D. Tosi, M. Tselengidou, A. Turcati, E. Unger, M. Usner, S. Vallecorsa, J. Vandenbroucke, N. van Eijndhoven, S. Vanheule, M. van Rossem, J. van Santen, J. Veenkamp, M. Vehring, M. Voge, M. Vraeghe, C. Walck, A. Wallace, M. Wallraff, N. Wandkowsky, Ch. Weaver, C. Wendt, S. Westerhoff, B. J. Whelan, S. Wickmann, K. Wiebe, C. H. Wiebusch, L. Wille, D. R. Williams, L. Wills, H. Wissing, M. Wolf, T. R. Wood, E. Woolsey, K. Woschnagg, D. L. Xu, X. W. Xu, Y. Xu, J. P. Yanez, G. Yodh, S. Yoshida and M. Zoll (IceCube Collaboration)

On self-adaptive features in real-parameter evolutionary algorithms

Abstract: Due to the flexibility in adapting to different fitness landscapes, self-adaptive evolutionary algorithms (SA-EAs) have been gaining popularity in the recent past. In this paper, we postulate the properties that SA-EA operators should have for successful applications in real-valued search spaces. Specifically, population mean and variance of a number of SA-EA operators such as various real-parameter crossover operators and self-adaptive evolution strategies are calculated for this purpose. Simulation results are shown to verify the theoretical calculations. The postulations and population variance calculations explain why self-adaptive genetic algorithms and evolution strategies have shown similar performance in the past and also suggest appropriate strategy parameter values, which must be chosen while applying and comparing different SA-EAs.