Extensions to behavioral genetic programming

Author(s)
Fine, Steven B
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Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Advisor
Una-May O'Reilly.
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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. http://dspace.mit.edu/handle/1721.1/7582
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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).
 
Date issued
2017
URI
http://hdl.handle.net/1721.1/112846
Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Publisher
Massachusetts Institute of Technology
Keywords
Electrical Engineering and Computer Science.
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