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Sensitivity to parameters and general applicability of genetic algorithms and simulated annealing algorithms for mapping data to multicomputers. (c1994)

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dc.contributor.author Kawash, Jalal Y.
dc.date.accessioned 2011-01-07T10:18:51Z
dc.date.available 2011-01-07T10:18:51Z
dc.date.copyright 1994 en_US
dc.date.issued 1/7/2011
dc.date.submitted 1994-12
dc.identifier.uri http://hdl.handle.net/10725/195
dc.description Includes bibliographical references. en_US
dc.description.abstract We analyze the sensitivity to parameters and the general applicability of genetic algorithms and simulated annealing algorithms for mapping data to distributed-memory multicomputers, using the loosely synchronous computation model. The analysis includes sensitivity to user parameters, fault tolerance capability, and applicability to different multicomputer topologies. The user parameters are either objective function dependent or algorithm dependent. The fault tolerance capability is demonstrated by using the mapping algorithms for mapping data to a multicomputer that has some failed processors. We assume a hypercube multicomputer architecture in most experiments. However, comparative results for mesh, array, ring, tree, star graph, and fully connected topologies are presented. The mapping algorithms used are sequential hybrid genetic algorithm, versions of a distributed genetic algorithm, sequential simulated annealing algorithm, and a simulated parallel simulated annealing algorithm. The experimental results verifY that these algorithms are insensitive to user parameters in wide ranges, completely fault tolerant, and unbiased towards particular multicomputer topologies. These results support the conjecture that physical optimization algorithms are flexible and have general applicability, where these properties are necessary for the automation of the mapping process. en_US
dc.language.iso en en_US
dc.subject Algorithms en_US
dc.subject Computer graphics en_US
dc.subject Parallel processing (Electronic computers) en_US
dc.subject Combinatorial optimization en_US
dc.subject Parallel computers en_US
dc.title Sensitivity to parameters and general applicability of genetic algorithms and simulated annealing algorithms for mapping data to multicomputers. (c1994) en_US
dc.type Thesis en_US
dc.term.submitted Fall en_US
dc.author.degree MS in Computer Science en_US
dc.author.school Arts and Sciences en_US
dc.author.commembers Dr. Wadih Juriedini
dc.author.commembers Dr. George Nasr
dc.author.woa RA en_US
dc.description.physdesc 1 bound copy: 1 v. (various pagings); ill., tables available at RNL. en_US
dc.author.division Computer Science en_US
dc.author.advisor Dr. Nashat Mansour
dc.identifier.doi https://doi.org/10.26756/th.1994.4 en_US
dc.publisher.institution Lebanese American University en_US
dc.author.affiliation Lebanese American University en_US


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