Design and implementation of evolutionary computation algorithms for volunteer compute networks
Name
894248603-MIT.pdf
Description
Full printable version
Size
5.43 MB
Format
Adobe PDF
Checksum (MD5)
cbda44f3d6123e97b30b421bfe1e904c
Author(s)
Mbagwu, Otitochi (Otitochi E.)
Advisor(s)
Una-May 0' Reilly and Erik Hemberg.
Date Issued
2014
Publisher
Massachusetts Institute of Technology
Abstract
We implemented a distributed evolutionary computation system titled EvoGPJ Star (EGS) and deployed the system onto Boinc, a volunteer computing network (VCN). Evolutionary computation is computationally expensive and VCN allows more cost-effective cluster computing since resources are donated. In addition, we believe that the design similarities between EGS and our chosen VCN (Boinc) would allow for easy integration of the two systems. EGS follows a centralized design pattern, with multiple engines communicating with a central coordinator and case server. The coordinator synchronizes up engines to run experiments and also stores and distributes individual solutions among engines. The engine-coordinator model creates a scalable (engines can be easily added) and robust (can continue to operate if nodes fail) system. For our experiment we chose rule-based classification. We saw the distributed EGS solutions (standard and Boinc) outperform the single-engine system. Deploying the system to Boinc revealed some design conflicts between Boinc and EGS experimentation. These conflicts stemmed from the asynchronous and asymmetric nature of VCNs.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, June 2014.
Cataloged from PDF version of thesis. "May 23, 2014."
Includes bibliographical references (page 37).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
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