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Automated File Editing Using Genetic Programming
 Sedláček, Marek ; Vašíček, Zdeněk (oponent) ; Sekanina, Lukáš (vedoucí práce)
File editing is an integral part of today's work for many people, but not everyone has programming skills or deep knowledge of editing tools to make their editing efficient and quick. This is exactly what the program presented in this thesis -- Ebe -- is trying to solve. Ebe takes snippets of file edits done by the user and using genetic programming it finds the correct algorithm to transform the whole file or even multiple files into the desired output.  Ebe consist of multiple parts, which had to be designed and implemented to achieve its goals. For this purpose a new programming language was designed to suite file editing and work well with genetic programming, an interpreter for this language was implemented as well as a compiler that uses genetic programming to synthesize the editing algorithm based on given examples. Ebe was then tested with other tools for file editing. These experiment focused on the overall editing speed and Ebe ended up having better editing times than Python 3 and similar editing times as the language AWK in most experiments. These experiments proved, that for many frequent editing tasks Ebe has a potential as an alternative tool for file editing.
Úplný záznam
Automated File Editing Using Genetic Programming
 Sedláček, Marek ; Vašíček, Zdeněk (oponent) ; Sekanina, Lukáš (vedoucí práce)
File editing is an integral part of today's work for many people, but not everyone has programming skills or deep knowledge of editing tools to make their editing efficient and quick. This is exactly what the program presented in this thesis -- Ebe -- is trying to solve. Ebe takes snippets of file edits done by the user and using genetic programming it finds the correct algorithm to transform the whole file or even multiple files into the desired output.  Ebe consist of multiple parts, which had to be designed and implemented to achieve its goals. For this purpose a new programming language was designed to suite file editing and work well with genetic programming, an interpreter for this language was implemented as well as a compiler that uses genetic programming to synthesize the editing algorithm based on given examples. Ebe was then tested with other tools for file editing. These experiment focused on the overall editing speed and Ebe ended up having better editing times than Python 3 and similar editing times as the language AWK in most experiments. These experiments proved, that for many frequent editing tasks Ebe has a potential as an alternative tool for file editing.
Úplný záznam

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