On the Trade-Off between Population Size and Number of Generations in GP for Program Synthesis
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{briesch:2023:GECCOcomp,
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author = "Martin Briesch and Dominik Sobania and
Franz Rothlauf",
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title = "On the {Trade-Off} between Population Size and Number
of Generations in {GP} for Program Synthesis",
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booktitle = "Proceedings of the 2023 Genetic and Evolutionary
Computation Conference",
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year = "2023",
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editor = "Sara Silva and Luis Paquete and Leonardo Vanneschi and
Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and
Arnaud Liefooghe and Bing Xue and Ying Bi and
Nelishia Pillay and Irene Moser and Arthur Guijt and
Jessica Catarino and Pablo Garcia-Sanchez and
Leonardo Trujillo and Carla Silva and Nadarajen Veerapen",
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pages = "535--538",
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address = "Lisbon, Portugal",
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series = "GECCO '23",
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month = "15-19 " # 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, program
synthesis, crossover, population size, generations,
mutation: Poster",
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isbn13 = "9798400701191",
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DOI = "doi:10.1145/3583133.3590681",
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size = "4 pages",
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abstract = "When using genetic programming for program synthesis,
we are usually constrained by a computational budget
measured in program executions during evolution. The
computational budget is influenced by the choice of
population size and number of generations per run
leading to a trade-off between both possibilities. To
better understand this trade-off, we analyze the
effects of different combinations of population sizes
and number of generations on performance. Further, we
analyze how the use of different variation operators
affects this trade-off. We conduct experiments on a
range of common program synthesis benchmarks and find
that using larger population sizes lead to a better
search performance. Additionally, we find that using
high probabilities for crossover and mutation lead to
higher success rates. Focusing on only crossover or
using only mutation usually leads to lower search
performance. In summary, we find that large populations
combined with high mutation and crossover rates yield
highest GP performance for program synthesis
approaches.",
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notes = "GECCO-2023 A Recombination of the 32nd International
Conference on Genetic Algorithms (ICGA) and the 28th
Annual Genetic Programming Conference (GP)",
- }
Genetic Programming entries for
Martin Briesch
Dominik Sobania
Franz Rothlauf
Citations