Structured Grammatical Evolution applied to program synthesis

This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.

Bibliographic Details
Main Author: Zhang, Andrew H.,M. Eng.Massachusetts Institute of Technology.
Other Authors: Una-May O'Reilly.
Format: Thesis
Language:eng
Published: Massachusetts Institute of Technology 2019
Subjects:
Online Access:https://hdl.handle.net/1721.1/122995
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author Zhang, Andrew H.,M. Eng.Massachusetts Institute of Technology.
author2 Una-May O'Reilly.
author_facet Una-May O'Reilly.
Zhang, Andrew H.,M. Eng.Massachusetts Institute of Technology.
author_sort Zhang, Andrew H.,M. Eng.Massachusetts Institute of Technology.
collection MIT
description This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
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spelling mit-1721.1/1229952019-11-22T03:13:22Z Structured Grammatical Evolution applied to program synthesis SGE applied to program synthesis Zhang, Andrew H.,M. Eng.Massachusetts Institute of Technology. Una-May O'Reilly. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Electrical Engineering and Computer Science. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019 Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (page 27). Grammatical Evolution (GE) is an evolutionary algorithm that is gaining popularity due to its ability to solve problems where it would be impossible to explore every solution within a realistic time. Structured Grammatical Evolution (SGE) was developed to overcome some of the shortcomings of GE, such as locality issues as well as wrapping around the genotype to complete the phenotype. In this paper, we apply SGE to program synthesis, where the computer must generate code to solve algorithmic problems. SGE was improved upon, because the current definition of SGE does not work. Given that the solution space is very large for possible codes, we aim to improve the efficiency of GE in converging to the correct solution. We present a method in which to remove cycles from a grammar for SGE, to be able to make sure that a genotype matches to a phenotype with reusing parts of the genotype, and analyze results to shed insight on future improvements. by Andrew H. Zhang. M. Eng. M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science 2019-11-22T00:00:49Z 2019-11-22T00:00:49Z 2019 2019 Thesis https://hdl.handle.net/1721.1/122995 1127291873 eng 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 27 pages application/pdf Massachusetts Institute of Technology
spellingShingle Electrical Engineering and Computer Science.
Zhang, Andrew H.,M. Eng.Massachusetts Institute of Technology.
Structured Grammatical Evolution applied to program synthesis
title Structured Grammatical Evolution applied to program synthesis
title_full Structured Grammatical Evolution applied to program synthesis
title_fullStr Structured Grammatical Evolution applied to program synthesis
title_full_unstemmed Structured Grammatical Evolution applied to program synthesis
title_short Structured Grammatical Evolution applied to program synthesis
title_sort structured grammatical evolution applied to program synthesis
topic Electrical Engineering and Computer Science.
url https://hdl.handle.net/1721.1/122995
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