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dc.contributor.authorHuang, Jinling
dc.date.accessioned2014-03-04T21:58:55Z
dc.date.available2014-03-04T21:58:55Z
dc.date.issued2002-08
dc.identifier.otherhuang_jinling_200208_ms
dc.identifier.urihttp://purl.galileo.usg.edu/uga_etd/huang_jinling_200208_ms
dc.identifier.urihttp://hdl.handle.net/10724/29202
dc.description.abstractThis study designs and implements the parallel algorithms with several optimization approaches including simulated annealing, large step Markov chains (LSMC), evolutionary programming, and genetic algorithms, for a physical mapping problem based on the maximum likelihood estimator model. The parallel algorithms are implemented using a combination of inter-process communication via message passing and shared memory multithreaded programming and have provided good performance. Genetic algorithms using a heuristic crossover operator yields better results in terms of both solution accuracy and performance compared to the simulated annealing, LSMC and evolutionary programming approaches.
dc.languageParallel computing for reconstructing physical maps of chromosomes
dc.publisheruga
dc.rightspublic
dc.subjectPhysical Mapping
dc.subjectParallel Computing
dc.subjectMaximum Likelihood
dc.subjectEstimator
dc.subjectSimulated Annealing
dc.subjectLarge Step Markov Chains,
dc.subjectEvolutionary Programming
dc.subjectGenetic Algorithm
dc.subjectMPI
dc.subjectPthreads
dc.titleParallel computing for reconstructing physical maps of chromosomes
dc.typeThesis
dc.description.degreeMS
dc.description.departmentComputer Science
dc.description.majorComputer Science
dc.description.advisorSuchendra Bhandarkar
dc.description.committeeSuchendra Bhandarkar
dc.description.committeeHamid Arabnia
dc.description.committeeThiab Taha


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