{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/122995"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/122995","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Structured Grammatical Evolution applied to program synthesis","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_html":"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_has_math":false,"creators":["Zhang, Andrew H.,M. Eng.Massachusetts Institute of Technology."],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. 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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."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M. Eng."]},{"key":"dc:title","label":"Title","values":["Structured Grammatical Evolution applied to program synthesis"]}]}],"canonical_facts":{"dc:contributor.advisor":["Una-May O'Reilly."],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science","EECS"],"dc:contributor.other":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science."],"dc:creator":["Zhang, Andrew H.,M. 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