8 resultados para Union diversity
em Boston University Digital Common
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The print copy of this sermon is held by Pitts Theology Library. The Pitts Theology Library's digital copy was produced as part of the ATLA/ATS Cooperative Digital Resources Initiative (CDRI), funded by the Luce Foundation. Reproduction note: Electronic reproduction. Atlanta, Georgia : Pitts Theology Library, Emory University, 2003. (Thanksgiving Day Sermons, ATLA Cooperative Digital Resources Initiative, CDRI). Joint CDRI project by: Andover-Harvard Library (Harvard Divinity School), Pitts Theology Library (Emory University), and Princeton Theological Seminary Libraries.
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http://www.archive.org/details/addressesofrevdr00parkuoft
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http://www.archive.org/details/theunionmissiony00spuruoft
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http://www.archive.org/details/catholicgrieva00mealrich
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This is a postprint (author's final draft) version of an article published in the journal Social Compass in 2010. The final version of this article may be found at http://dx.doi.org/10.1177/0037768610362406 (login may be required). The version made available in OpenBU was supplied by the author.
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This paper investigates the power of genetic algorithms at solving the MAX-CLIQUE problem. We measure the performance of a standard genetic algorithm on an elementary set of problem instances consisting of embedded cliques in random graphs. We indicate the need for improvement, and introduce a new genetic algorithm, the multi-phase annealed GA, which exhibits superior performance on the same problem set. As we scale up the problem size and test on \hard" benchmark instances, we notice a degraded performance in the algorithm caused by premature convergence to local minima. To alleviate this problem, a sequence of modi cations are implemented ranging from changes in input representation to systematic local search. The most recent version, called union GA, incorporates the features of union cross-over, greedy replacement, and diversity enhancement. It shows a marked speed-up in the number of iterations required to find a given solution, as well as some improvement in the clique size found. We discuss issues related to the SIMD implementation of the genetic algorithms on a Thinking Machines CM-5, which was necessitated by the intrinsically high time complexity (O(n3)) of the serial algorithm for computing one iteration. Our preliminary conclusions are: (1) a genetic algorithm needs to be heavily customized to work "well" for the clique problem; (2) a GA is computationally very expensive, and its use is only recommended if it is known to find larger cliques than other algorithms; (3) although our customization e ort is bringing forth continued improvements, there is no clear evidence, at this time, that a GA will have better success in circumventing local minima.
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The CIL compiler for core Standard ML compiles whole programs using a novel typed intermediate language (TIL) with intersection and union types and flow labels on both terms and types. The CIL term representation duplicates portions of the program where intersection types are introduced and union types are eliminated. This duplication makes it easier to represent type information and to introduce customized data representations. However, duplication incurs compile-time space costs that are potentially much greater than are incurred in TILs employing type-level abstraction or quantification. In this paper, we present empirical data on the compile-time space costs of using CIL as an intermediate language. The data shows that these costs can be made tractable by using sufficiently fine-grained flow analyses together with standard hash-consing techniques. The data also suggests that non-duplicating formulations of intersection (and union) types would not achieve significantly better space complexity.
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Forwarding in DTNs is a challenging problem. We focus on the specific issue of forwarding in an environment where mobile devices are carried by people in a restricted physical space (e.g. a conference) and contact patterns are not predictable. We show for the first time a path explosion phenomenon between most pairs of nodes. This means that, once the first path reaches the destination, the number of subsequent paths grows rapidly with time, so there usually exist many near-optimal paths. We study the path explosion phenomenon both analytically and empirically. Our results highlight the importance of unequal contact rates across nodes for understanding the performance of forwarding algorithms. We also find that a variety of well-known forwarding algorithms show surprisingly similar performance in our setting and we interpret this fact in light of the path explosion phenomenon.