Multi-Objective Island Model Genetic Programming for Predicting the Stokes Flow around a Sphere
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Reuter:2023:SSCI,
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author = "Julia Reuter and Pravin Pandey and Sanaz Mostaghim",
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booktitle = "2023 IEEE Symposium Series on Computational
Intelligence (SSCI)",
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title = "Multi-Objective Island Model Genetic Programming for
Predicting the Stokes Flow around a Sphere",
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year = "2023",
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pages = "1485--1490",
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abstract = "This paper is aimed at enhancing the success rate of
Genetic Programming (GP) algorithms for symbolic
regressions. It is shown that the outcome of GP
algorithms over several runs can lead to an optimal
solution for such problems, but the success rate, i.e.,
the number of successful runs, is sometimes small. We
address this issue by proposing multi-objective and
island model (IM) optimisation for GP. We study the
influence of various objective functions and IM
configurations on the success rates and present 36
algorithm variants, which are tasked with solving two
benchmark equations from the fluid mechanics area. This
specific benchmark problem has been previously shown to
suffer from a low success rate and high variations
between the results of multiple runs. Our experiments
show a strong influence of the objective functions on
the success rate. The additional IM implementation
improves the results for some objectives. The algorithm
with the highest success rate on the more complex
benchmark problem employs both, multiple objectives and
IM.",
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keywords = "genetic algorithms, genetic programming, Fluids,
Computational modelling, Benchmark testing, Predictive
models, Linear programming, Prediction algorithms",
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DOI = "doi:10.1109/SSCI52147.2023.10371955",
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ISSN = "2472-8322",
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month = dec,
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notes = "Also known as \cite{10371955}",
- }
Genetic Programming entries for
Julia Reuter
Pravin Pandey
Sanaz Mostaghim
Citations