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dc.contributor.advisorLivesey, Mike
dc.contributor.authorPaterson, Norman R.
dc.coverage.spatial241 p.en_US
dc.date.accessioned2018-07-05T12:01:14Z
dc.date.available2018-07-05T12:01:14Z
dc.date.issued2003
dc.identifier.urihttps://hdl.handle.net/10023/14984
dc.description.abstractThis thesis presents Genetic Algorithm for Deriving Software (Gads), a new technique for genetic programming. Gads combines a conventional genetic algorithm with a context-sensitive grammar. The key to Gads is the onto genic mapping, which converts a genome from an array of integers to a correctly typed program in the phenotype language defined by the grammar. A new type of grammar, the reflective attribute grammar (rag), is introduced. The rag is an extension of the conventional attribute grammar, which is designed to produce valid sentences, not to recognize or parse them. Together, Gads and rags provide a scalable solution for evolving type-correct software in independently-chosen context-sensitive languages. The statistics of performance comparison is investigated. A method for representing a set of genetic programming systems or problems on a cladogram is presented. A method for comparing genetic programming systems or problems on a single rational scale is proposed.en_US
dc.language.isoenen_US
dc.publisherUniversity of St Andrews
dc.subject.lccQA76.623P2
dc.subject.lcshGenetic programming (Computer science)en
dc.titleGenetic programming with context-sensitive grammarsen_US
dc.typeThesisen_US
dc.type.qualificationlevelDoctoralen_US
dc.type.qualificationnamePhD Doctor of Philosophyen_US
dc.publisher.institutionThe University of St Andrewsen_US


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