Teaching GP to program like a human software developer: using perplexity pressure to guide program synthesis approaches

Created by W.Langdon from gp-bibliography.bib Revision:1.7778

@InProceedings{Sobania:2019:GECCO,

• author = "Dominik Sobania and Franz Rothlauf",
• title = "Teaching {GP} to program like a human software developer: using perplexity pressure to guide program synthesis approaches",
• booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference",
• year = "2019",
• editor = "Manuel Lopez-Ibanez and Thomas Stuetzle and Anne Auger and Petr Posik and Leslie {Peprez Caceres} and Andrew M. Sutton and Nadarajen Veerapen and Christine Solnon and Andries Engelbrecht and Stephane Doncieux and Sebastian Risi and Penousal Machado and Vanessa Volz and Christian Blum and Francisco Chicano and Bing Xue and Jean-Baptiste Mouret and Arnaud Liefooghe and Jonathan Fieldsend and Jose Antonio Lozano and Dirk Arnold and Gabriela Ochoa and Tian-Li Yu and Holger Hoos and Yaochu Jin and Ting Hu and Miguel Nicolau and Robin Purshouse and Thomas Baeck and Justyna Petke and Giuliano Antoniol and Johannes Lengler and Per Kristian Lehre",
• isbn13 = "978-1-4503-6111-8",
• pages = "1065--1074",
• address = "Prague, Czech Republic",
• DOI = "doi:10.1145/3321707.3321738",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• month = "13-17 " # jul,
• organisation = "SIGEVO",
• keywords = "genetic algorithms, genetic programming, Genetic improvement, grammatical evolution, Software synthesis, Mining software repositories, Language model, SBSE, Search-based software engineering, Design and analysis of algorithms, GitHub",
• size = "10 pages",
• abstract = "Program synthesis is one of the relevant applications of GP with a strong impact on new fields such as genetic improvement. In order for synthesized code to be used in real-world software, the structure of the programs created by GP must be maintainable. We can teach GP how real-world software is built by learning the relevant properties of mined human-coded software: which can be easily accessed through repository hosting services such as GitHub. So combining program synthesis and repository mining is a logical step. In this paper, we analyse if GP can write programs with properties similar to code produced by human software developers. First, we compare the structure of functions generated by different GP initialization methods to a mined corpus containing real-world software. The results show that the studied GP initialization methods produce a totally different combination of programming language elements in comparison to real-world software. Second, we propose perplexity pressure and analyze how its use changes the properties of code produced by GP. The results are very promising and show that we can guide the search to the desired program structure. Thus, we recommend using perplexity pressure as it can be easily integrated in various search-based algorithms.",
• notes = "Also known as \cite{3321738} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for Dominik Sobania Franz Rothlauf

Citations