2026-09-18T19:00:39Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/854982026-06-06T00:48:53Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Kalyan Veeramachaneni and Una-May O'Reilly.
Sherry, Dylan J. (Dylan Jacob)
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2014-03-06T15:46:22Z
2014-03-06T15:46:22Z
2013
2013
http://hdl.handle.net/1721.1/85498
871002391
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2013.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 105-107).
This thesis presents FlexGP 2.0, a distributed cloud-backed machine learning system. FlexGP 2.0 features multiple levels of parallelism which provide a significant improvement in accuracy v.s. elapsed time. The amount of computational resources in FlexGP 2.0 can be scaled along several dimensions to support large, complex data. FlexGP 2.0's core genetic programming (GP) learner includes multithreaded C++ model evaluation and a multi-objective optimization algorithm which is extensible to pursue any number of objectives simultaneously in parallel. FlexGP 2.0 parallelizes the entire learner to obtain a large distributed population size and leverages communication between learners to increase performance via transferral of search progress between learners. FlexGP 2.0 factors training data to boost performance and enable support for increased data size and complexity. Several experiments are performed which verify the efficacy of FlexGP 2.0's multilevel parallelism. Experiments run on a large dataset from a real-world regression problem. The results demonstrate both less time to achieve the same accuracy and overall increased accuracy, and illustrate the value of FlexGP 2.0 as a platform for machine learning.
by Dylan J. Sherry.
M.Eng.
107 pages
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
FlexGP 2.0 : multiple levels of parallelism in distributed machine learning via genetic programming
Multiple levels of parallelism in distributed machine learning via genetic programming
Thesis
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<Abstract>This thesis presents FlexGP 2.0, a distributed cloud-backed machine learning system. FlexGP 2.0 features multiple levels of parallelism which provide a significant improvement in accuracy v.s. elapsed time. The amount of computational resources in FlexGP 2.0 can be scaled along several dimensions to support large, complex data. FlexGP 2.0's core genetic programming (GP) learner includes multithreaded C++ model evaluation and a multi-objective optimization algorithm which is extensible to pursue any number of objectives simultaneously in parallel. FlexGP 2.0 parallelizes the entire learner to obtain a large distributed population size and leverages communication between learners to increase performance via transferral of search progress between learners. FlexGP 2.0 factors training data to boost performance and enable support for increased data size and complexity. Several experiments are performed which verify the efficacy of FlexGP 2.0's multilevel parallelism. Experiments run on a large dataset from a real-world regression problem. The results demonstrate both less time to achieve the same accuracy and overall increased accuracy, and illustrate the value of FlexGP 2.0 as a platform for machine learning.</Abstract>
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