Extensions to behavioral genetic programming
Name
1015240188-MIT.pdf
Description
Full printable version
Size
401.29 KB
Format
Adobe PDF
Checksum (MD5)
dfcb430af6102cc50fa41f78f8191ee4
Author(s)
Fine, Steven B
Advisor(s)
Una-May O'Reilly.
Date Issued
2017
Publisher
Massachusetts Institute of Technology
Abstract
In this work I introduce genetic programming [5] as a general technique to produce programs with arbitrary behavior. I discuss genetic programming and its application the task of symbolic regression. I introduce behavioral genetic programming [6] as an extension to genetic programming and explore various extensions to it. The codebase that I build is made sufficiently flexible to easily accommodate future adaptions to the behavioral genetic programming methodology. I test the performance of the implementation of behavioral genetic programming along with several extensions.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (page 55).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.
Persistent DSpace Link
