Evolutionary Optimization of a Focused Ultrasound Propagation Predictor Neural Network
Created by W.Langdon from
gp-bibliography.bib Revision:1.7325
- @InProceedings{chlebik:2023:GECCOcomp,
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author = "Jakub Chlebik and Jiri Jaros",
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title = "Evolutionary Optimization of a Focused Ultrasound
Propagation Predictor Neural Network",
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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 = "635--638",
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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, cartesian
genetic programming, ultrasound propagation predictor,
evolutionary design, evolutionary optimisation:
Poster",
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isbn13 = "9798400701191",
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DOI = "
doi:10.1145/3583133.3590661",
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size = "4 pages",
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abstract = "The search for the optimal treatment plan of a focused
ultrasound-based procedure is a complex multi-modal
problem, trying to deliver the solution in clinically
relevant time while not sacrificing the precision below
a critical threshold. To test a solution, many
computationally expensive simulations must be
evaluated, often thousands of times. The recent
renaissance of machine learning could provide an answer
to this. Indeed, a state-of-the-art neural predictor of
Acoustic Propagation through a human skull was
published recently, speeding up the simulation
significantly. The utilized architecture, however,
could use some improvements in precision. To explore
the design more deeply, we made an attempt to improve
the solver by use of an evolutionary algorithm,
challenging the importance of different building
blocks. Utilizing Genetic Programming, we improved
their solution significantly, resulting in a solver
with approximately an order of magnitude better RMSE of
the predictor, while still delivering solutions in a
reasonable time frame. Furthermore, a second study was
conducted to gauge the effects of the multi-resolution
encoding on the precision of the network, providing
interesting topics for further research on the effects
of the memory blocks and convolution kernel sizes for
PDE RCNN solvers.",
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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
Jakub Chlebik
Jiri Jaros
Citations