Evolutionary Optimization of a Focused Ultrasound Propagation Predictor Neural Network

Created by W.Langdon from gp-bibliography.bib Revision:1.8120

@InProceedings{chlebik:2023:GECCOcomp,

• author = "Jakub Chlebik and Jiri Jaros",
• title = "Evolutionary Optimization of a Focused Ultrasound Propagation Predictor Neural Network",
• booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference",
• year = "2023",
• 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",
• pages = "635--638",
• address = "Lisbon, Portugal",
• series = "GECCO '23",
• month = "15-19 " # jul,
• organisation = "SIGEVO",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, cartesian genetic programming, ultrasound propagation predictor, evolutionary design, evolutionary optimisation: Poster",
• isbn13 = "9798400701191",
• DOI = "doi:10.1145/3583133.3590661",
• size = "4 pages",
• 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 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. Using 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.",
• 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

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