A Pareto Front Based Evolutionary Model for Airfoil Self-Noise Prediction
Created by W.Langdon from
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- @InProceedings{Tahmassebi:2018:CECa,
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author = "Amirhessam Tahmassebi and Amir H. Gandomi and
Anke Meyer-Baese",
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title = "A Pareto Front Based Evolutionary Model for Airfoil
Self-Noise Prediction",
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booktitle = "2018 IEEE Congress on Evolutionary Computation (CEC)",
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year = "2018",
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editor = "Marley Vellasco",
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address = "Rio de Janeiro, Brazil",
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month = "8-13 " # jul,
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publisher = "IEEE",
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keywords = "genetic algorithms, genetic programming,
Multi-Objective",
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URL = "https://ieeexplore.ieee.org/abstract/document/8477987",
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DOI = "doi:10.1109/CEC.2018.8477987",
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abstract = "According to NASA's report on the technologies that
could reduce external aircraft noise by 10 dB, a
challenge equally as important as finding approaches on
airframe noise reduction is the demand to bring up
strategies by which airframe noise can be predicted
both accurately and rapidly. One of the components of
the overall airframe noise is the self-noise of the
airfoil itself. In this paper, an evolutionary symbolic
implementation for airfoil self-noise prediction was
proposed. Multi-objective genetic programming as a
subset of evolutionary computation along with adaptive
regression by mixing algorithm was used to create an
executable fused model. The developed model was tested
on the airfoil self-noise database and the performance
of the developed model was compared to the previous
works and benchmark machine learning algorithms. The
reasonable results suggest that the proposed model can
be applied to noise generation by low-Mach-number
turbulent flows in aerospace, automotive, underwater,
and wind turbine acoustic communities.",
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notes = "WCCI2018",
- }
Genetic Programming entries for
Amirhessam Tahmassebi
A H Gandomi
Anke Meyer-Baese
Citations