- author = "Erik Fredericks and Byron DeVries and Reihaneh Hariri",
- title = "Databending as a Target for Genetic Improvement",
- booktitle = "15th International Workshop on Genetic Improvement @ICSE 2026",
- year = "2026",
- editor = "Aymeric Blot and Oliver Krauss",
- address = "Rio de Janeiro",
- month = "13 " # apr,
- keywords = "genetic algorithms, genetic programming, genetic improvement, computer art, glitch art, software engineering, evolutionary computation, generative art, PyGGI 2.0, tobloef glitch-tool, software maintence, tabu search, diversity",
-
URL = "
http://gpbib.cs.ucl.ac.uk/gi2026/Fredericks_2026_GI.pdf",
-
code_url = "
https://github.com/efredericks/pyggi/tree/gi2026",
-
data_url = "https://zenodo.org/records/17407891",
-
data_url = "https://doi.org/10.5281/zenodo.17407891",
- size = "5 pages",
- abstract = "Genetic improvement (GI) is typically used as an approach for optimizing source code for a particular task, where examples include minimizing runtime, improving energy efficiency, and reducing memory footprint. As with any evolutionary algorithm, GI will optimize for a desired fitness objective and can be coerced into objectives that are not strictly for the improvement of software. This paper presents in-progress work towards using GI as a method for generating glitch art, or artwork that is created by intentionally corrupting files (i.e., databending) to provide an aesthetic or emotional experience. We use the PYGGI framework to explore how code patches can impact an existing glitch art framework, with the goal being to find new and interesting outputs that are different from what can be found with random search.",
-
notes = "'maximizing the number of patched lines of code'
'Rosa Menkman wrote The Glitch Moment(um)'
EvoMusArt conference. improve_python. parameters within glitch-tool source code. imagehash => h_diff (better than pixel difference?) 'opened in Krita and exported as PNG'
GI @ ICSE 2026,
Grand Valley State University, USA.",
