On domain knowledge and novelty to improve program synthesis performance with grammatical evolution

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Hemberg:2019:GECCO,

• author = "Erik Hemberg and Jonathan Kelly and Una-May O'Reilly",
• title = "On domain knowledge and novelty to improve program synthesis performance with grammatical evolution",
• 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",
• pages = "1039--1046",
• address = "Prague, Czech Republic",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• month = "13-17 " # jul,
• organisation = "SIGEVO",
• keywords = "genetic algorithms, genetic programming, grammatical evolution, Multi-agent systems, grammar, program synthesis, novelty",
• isbn13 = "978-1-4503-6111-8",
• URL = "https://alfagroup.csail.mit.edu/sites/default/files/documents/2019Domain_Knowledge_and_Novelty_to_Improve_Program_Synthesis_Performance_with_Grammatical_Evolution.pdf",
• DOI = "doi:10.1145/3321707.3321865",
• size = "8 pages",
• abstract = "Programmers solve coding problems with the support of both programming and problem specific knowledge. They integrate this domain knowledge to reason by computational abstraction. Correct and readable code arises from sound abstractions and problem solving. We attempt to transfer insights from such human expertise to genetic programming (GP) for solving automatic program synthesis. We draw upon manual and non-GP Artificial Intelligence methods to extract knowledge from synthesis problem definitions to guide the construction of the grammar that Grammatical Evolution uses and to supplement its fitness function. We examine the impact of using such knowledge on 21 problems from the GP program synthesis benchmark suite. Additionally, we investigate the compounding impact of this knowledge and novelty search. The resulting approaches exhibit improvements in accuracy on a majority of problems in the fields benchmark suite of program synthesis problems.",
• notes = "Also known as \cite{3321865} 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 Erik Hemberg Jonathan Kelly Una-May O'Reilly

Citations