Multi-Objective Island Model Genetic Programming for Predicting the Stokes Flow around a Sphere

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Reuter:2023:SSCI,

• author = "Julia Reuter and Pravin Pandey and Sanaz Mostaghim",
• booktitle = "2023 IEEE Symposium Series on Computational Intelligence (SSCI)",
• title = "Multi-Objective Island Model Genetic Programming for Predicting the Stokes Flow around a Sphere",
• year = "2023",
• pages = "1485--1490",
• 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.",
• keywords = "genetic algorithms, genetic programming, Fluids, Computational modelling, Benchmark testing, Predictive models, Linear programming, Prediction algorithms",
• DOI = "doi:10.1109/SSCI52147.2023.10371955",
• ISSN = "2472-8322",
• month = dec,
• notes = "Also known as \cite{10371955}",

}

Genetic Programming entries for Julia Reuter Pravin Pandey Sanaz Mostaghim

Citations