2026-09-19T02:00:46Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1229952026-06-06T00:55:33Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Una-May O'Reilly.
Zhang, Andrew H.,M. Eng.Massachusetts Institute of Technology.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2019-11-22T00:00:49Z
2019-11-22T00:00:49Z
2019
2019
https://hdl.handle.net/1721.1/122995
1127291873
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
27 pages
eng
Massachusetts Institute of Technology
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
Electrical Engineering and Computer Science.
Structured Grammatical Evolution applied to program synthesis
SGE applied to program synthesis
Thesis
application/pdf
2019-11-22T00:00:47Z
Publication
Master
EECS
unknown
unknown
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<Abstract>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.</Abstract>
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