Zugl.: Bremen, Univ., Diss. 2007

Thesis (Ph. D.)--University of Washington, 2007. / Vita. Includes bibliographical references (p. 94-98).

Universiẗat, Diss., 2001--Stuttgart.

Thesis (M.S.)--West Virginia University, 2006. / Title from document title page. Document formatted into pages; contains viii, 136 p. : ill. Includes abstract. Includes bibliographical references (p. 96-106).

Regensburg, Universiẗat, Diss., 2004.

Virginia Tech\'s Computational Bioinformatics and Bio-imaging Laboratory (CBIL) is exploring integrative network analysis techniques to identify subnetworks or genetic pathways that contribute to various cancers. Chen et. al. developed a bagging Markov random field (BMRF)-based approach which examines gene expression data with prior biological information to reliably identify significant genes and proteins. Using random resampling with replacement (bootstrapping or bagging) is essential to confident results but is computationally demanding as multiple iterations of the network identification (by simulated annealing) is required. The MATLAB implementation is computationally demanding, employs limited concurrency, and thus time prohibitive. Using strong software development discipline we optimize BMRF using algorithmic, compiler, and concurrency techniques (including Nvidia GPUs) to alleviate the wall clock time needed for analysis of large-scale genomic data. Particularly, we decompose the BMRF algorithm into functional blocks, implement the algorithm in C/C++ and further explore the C/C++ implementation with concurrency optimization. Experiments are conducted with simulation and real data to demonstrate that a significant speedup of BMRF can be achieved by exploiting concurrency opportunities. We believe that the experience gained by this research shall help pave the way for us to develop computationally efficient algorithms leveraging concurrency, enabling researchers to efficiently analyze larger-scale data sets essential for furthering cancer research. / Master of Science

No description available.

Yes / Proposes the application of a genetic algorithm (GA) and simulated annealing (SA) based hybrid approach for the scheduling of generator maintenance in power systems using an integer representation. The adapted approach uses the probabilistic acceptance criterion of simulated annealing within the genetic algorithm framework. A case study is formulated in this paper as an integer programming problem using a reliability-based objective function and typical problem constraints. The implementation and performance of the solution technique are discussed. The results in this paper demonstrate that the technique is more effective than approaches based solely on genetic algorithms or solely on simulated annealing. It therefore proves to be a valid approach for the solution of generator maintenance scheduling problems

Made available in DSpace on 2016-12-23T14:33:34Z (GMT). No. of bitstreams: 1 dissertacao.pdf: 412800 bytes, checksum: 479ec97937646fdcffeadd81d19f1b7a (MD5) Previous issue date: 2006-06-01 / Computar a solução ótima para uma unidade de problema MAX-SAT Ponderado (weighted maximum satisfiability) é difícil mesmo se cada cláusula contiver apenas dois literais. Neste trabalho, será descrita a implementação de uma nova heurística aplicada a instâncias de problema do tipo MAX-SAT Ponderado, mas perfeitamente extensível a outros problemas. Para comparação, serão geradas soluções para uma quantidade significativa de problemas e seus resultados serão comparados com os de outras heurísticas já desenvolvidas para esse tipo de problema, dentre elas as heurísticas consideradas "estado da arte", ou seja, heurísticas que têm obtido os melhores resultados no universo das heurísticas existentes.

Submitted by Beatriz Abreu (beatrizabreu@fgvmail.br) on 2016-08-29T18:33:06Z No. of bitstreams: 1 2016_MsC_Dissertação de Beatriz Abreu Foss de Oliveira.pdf: 1421679 bytes, checksum: 27c4f7c2d081c9676080d14bdd3e2cc2 (MD5) / Approved for entry into archive by ÁUREA CORRÊA DA FONSECA CORRÊA DA FONSECA (aurea.fonseca@fgv.br) on 2016-08-30T20:08:27Z (GMT) No. of bitstreams: 1 2016_MsC_Dissertação de Beatriz Abreu Foss de Oliveira.pdf: 1421679 bytes, checksum: 27c4f7c2d081c9676080d14bdd3e2cc2 (MD5) / Approved for entry into archive by Maria Almeida (maria.socorro@fgv.br) on 2016-09-02T13:26:30Z (GMT) No. of bitstreams: 1 2016_MsC_Dissertação de Beatriz Abreu Foss de Oliveira.pdf: 1421679 bytes, checksum: 27c4f7c2d081c9676080d14bdd3e2cc2 (MD5) / Made available in DSpace on 2016-09-02T13:27:18Z (GMT). No. of bitstreams: 1 2016_MsC_Dissertação de Beatriz Abreu Foss de Oliveira.pdf: 1421679 bytes, checksum: 27c4f7c2d081c9676080d14bdd3e2cc2 (MD5) Previous issue date: 2016-05-31 / Rankings are a recent tool being used in several fields, including in management. Their pervasive use is associated to the fields of behavior and decision making. Despite their constant usage, few research have tried to define the concept of ranking and the parameters to judge whether it is acceptable. In absence of a more precise understanding of what the term ranking means, its power is diminished as well as its purpose. Thus, in this work I present the characteristics, advantages and disadvantages of rankings. Further, I analyze patterns of behavior elicited in real rankings. To this end, I propose the use of simulated annealing to study the quality of convergence of rankings’ chosen dimensions. The graphical analyses suggest, at least, two different patterns of convergence for rankings. The categories were named after well behaved and poorly behaved rankings.