Genetic Programming Empowered Feature Construction towards Energy Efficient BVI Wearables
Created by W.Langdon from
gp-bibliography.bib Revision:1.8010
- @InProceedings{xu:2024:GECCO3,
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author = "Peijie Xu and Andy Song and Ke Wang",
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title = "Genetic Programming Empowered Feature Construction
towards Energy Efficient {BVI} Wearables",
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booktitle = "Proceedings of the 2024 Genetic and Evolutionary
Computation Conference",
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year = "2024",
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editor = "Ruhul Sarker and Patrick Siarry and Julia Handl and
Xiaodong Li and Markus Wagner and Mario Garza-Fabre and
Kate Smith-Miles and Richard Allmendinger and
Ying Bi and Grant Dick and Amir H Gandomi and
Marcella Scoczynski Ribeiro Martins and Hirad Assimi and
Nadarajen Veerapen and Yuan Sun and
Mario Andres Munyoz and Ahmed Kheiri and Nguyen Su and
Dhananjay Thiruvady and Andy Song and Frank Neumann and Carla Silva",
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pages = "1408--1416",
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address = "Melbourne, Australia",
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series = "GECCO '24",
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month = "14-18 " # 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, obstacle
avoidance datasets, obstacle detection, feature
construction, low energy consumption, efficient models,
hardware deployment, power usage, Real World
Applications",
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isbn13 = "979-8-4007-0494-9",
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DOI = "doi:10.1145/3638529.3654002",
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size = "9 pages",
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abstract = "Blind and visually impaired (BVI) individuals face
serious mobility-related risks daily due to the lack of
progression in hazard detection and assistive
technologies. Most existing techniques are demanding in
computational resources and energy consumption, yet
still struggle in real-time performance. The challenge
is particularly evident when deploying these techniques
on portable devices, on which lightweight coupled with
sustainable low battery usage is a must. Hence in this
study, we aim to leverage Genetic Programming (GP),
which is well known for its feature construction
capability, to develop more energy-efficient models
with better features. The objective is to find more
condensed features by GP, to reduce energy consumed and
inference time, but without significant accuracy loss
in obstacle detection from a head-mount wearable device
for BVIs. Features have been trained on a series of
in-door settings that represent a BVI person navigating
through corridors and furniture. Models with these
constructed features then are validated on actual
portable board deployment, as well as on Field
Programmable Gate Array simulation (FPGA). Comparative
analyses are conducted using a range of performance
metrics across models training by different learning
methods. The metrics include accuracy, model execution
time, prediction time, energy consumption, and hardware
resource usage. Our study demonstrates that
GP-constructed models can generally reduce energy
consumption and inference time with negligible accuracy
loss. Furthermore, it can build models with higher
accuracy than the benchmark, allowing users to adjust
between energy usage and accuracy according to their
needs.",
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notes = "GECCO-2024 RWA A Recombination of the 33rd
International Conference on Genetic Algorithms (ICGA)
and the 29th Annual Genetic Programming Conference
(GP)",
- }
Genetic Programming entries for
Peijie Xu
Andy Song
Ke Wang
Citations