Large Language Model-based Test Case Generation for GP Agents
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @InProceedings{jorgensen:2024:GECCO,
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author = "Steven Jorgensen and Giorgia Nadizar and
Gloria Pietropolli and Luca Manzoni and Eric Medvet and
Una-May O'Reilly and Erik Hemberg",
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title = "Large Language Model-based Test Case Generation for
{GP} Agents",
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booktitle = "Proceedings of the 2024 Genetic and Evolutionary
Computation Conference",
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year = "2024",
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editor = "Ting Hu and Aniko Ekart and Julia Handl and
Xiaodong Li and Markus Wagner and Mario Garza-Fabre and
Kate Smith-Miles and Richard Allmendinger and Ying Bi and
Grant Dick and Amir H Gandomi and
Marcella Scoczynski Ribeiro Martins and Hirad Assimi and
Nadarajen Veerapen and Yuan Sun and Mario Andres Munyoz and
Ahmed Kheiri and Nguyen Su and Dhananjay Thiruvady and Andy Song and
Frank Neumann and Carla Silva",
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pages = "914--923",
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address = "Melbourne, Australia",
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series = "GECCO '24",
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month = "14-18 " # jul,
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organisation = "SIGEVO",
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publisher = "Association for Computing Machinery",
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publisher_address = "New York, NY, USA",
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keywords = "genetic algorithms, genetic programming, linear GP,
large language models, curriculum learning",
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isbn13 = "979-8-4007-0494-9",
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DOI = "doi:10.1145/3638529.3654056",
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size = "10 pages",
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abstract = "Genetic programming (GP) is a popular problem-solving
and optimization technique. However, generating
effective test cases for training and evaluating GP
programs requires strong domain knowledge. Furthermore,
GP programs often prematurely converge on local optima
when given excessively difficult problems early in
their training. Curriculum learning (CL) has been
effective in addressing similar issues across different
reinforcement learning (RL) domains, but it requires
the manual generation of progressively difficult test
cases as well as their careful scheduling. In this
work, we leverage the domain knowledge and the strong
generative abilities of large language models (LLMs) to
generate effective test cases of increasing
difficulties and schedule them according to various
curricula. We show that by integrating a curriculum
scheduler with LLM-generated test cases we can
effectively train a GP agent player with
environments-based curricula for a single-player game
and opponent-based curricula for a multi-player game.
Finally, we discuss the benefits and challenges of
implementing this method for other problem domains.",
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notes = "GECCO-2024 GP A Recombination of the 33rd
International Conference on Genetic Algorithms (ICGA)
and the 29th Annual Genetic Programming Conference
(GP)",
- }
Genetic Programming entries for
Steven P Jorgensen
Giorgia Nadizar
Gloria Pietropolli
Luca Manzoni
Eric Medvet
Una-May O'Reilly
Erik Hemberg
Citations