Using Knowledge of Human-Generated Code to Bias the Search in Program Synthesis with Grammatical Evolution

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Schweim:2021:GECCOcomp,

• author = "Dirk Schweim and Erik Hemberg and Dominik Sobania and Una-May O'Reilly and Franz Rothlauf",
• title = "Using Knowledge of Human-Generated Code to Bias the Search in Program Synthesis with Grammatical Evolution",
• booktitle = "Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion",
• year = "2021",
• editor = "Francisco Chicano and Alberto Tonda and Krzysztof Krawiec and Marde Helbig and Christopher W. Cleghorn and Dennis G. Wilson and Georgios Yannakakis and Luis Paquete and Gabriela Ochoa and Jaume Bacardit and Christian Gagne and Sanaz Mostaghim and Laetitia Jourdan and Oliver Schuetze and Petr Posik and Carlos Segura and Renato Tinos and Carlos Cotta and Malcolm Heywood and Mengjie Zhang and Leonardo Trujillo and Risto Miikkulainen and Bing Xue and Aneta Neumann and Richard Allmendinger and Fuyuki Ishikawa and Inmaculada Medina-Bulo and Frank Neumann and Andrew M. Sutton",
• pages = "331--332",
• address = "internet",
• series = "GECCO '21",
• month = jul # " 10-14",
• organisation = "SIGEVO",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, Grammatical Evolution, SBSE, Program Synthesis, Software Synthesis, Grammar Guided Genetic Programming, Mining Software Repositories: Poster",
• isbn13 = "978-1-4503-8351-6",
• DOI = "doi:10.1145/3449726.3459548",
• size = "2 pages",
• abstract = "Recent studies show that program synthesis with GE produces code that has different structure compared to human-generated code, e.g., loops and conditions are hardly used. we extract knowledge from human-generated code to guide evolutionary search. We use a large code-corpus that was mined from the open software repository service GitHub and measure software metrics and properties describing the code-base. We use this knowledge to guide the search by incorporating a new selection scheme. Our new selection scheme favors programs that are structurally similar to the programs in the GitHub code-base. We find noticeable evidence that software metrics can help in guiding evolutionary search",
• notes = "

  GECCO-2021 A Recombination of the 30th International Conference on Genetic Algorithms (ICGA) and the 26th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for Dirk Schweim Erik Hemberg Dominik Sobania Una-May O'Reilly Franz Rothlauf

Citations