Evolutionary Design of Reduced Precision Levodopa-Induced Dyskinesia Classifiers

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F22%3APU144343" target="_blank" >RIV/00216305:26230/22:PU144343 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/chapter/10.1007/978-3-031-02056-8_6" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-02056-8_6</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-02056-8_6" target="_blank" >10.1007/978-3-031-02056-8_6</a>

Result language

angličtina

Original language name

Evolutionary Design of Reduced Precision Levodopa-Induced Dyskinesia Classifiers

Original language description

Parkinson's disease is one of the most common neurological conditions whose symptoms are usually treated with a drug containing levodopa. To minimise levodopa side effects, i.e. levodopa-induced dyskinesia (LID), it is necessary to correctly manage levodopa dosage. This article covers an application of cartesian genetic programming (CGP) to assess LID based on time series collected using accelerators attached to the patient's body. Evolutionary design of reduced precision classifiers of LID is investigated in order to find a hardware-efficient classifier together with classification accuracy as close as possible to a baseline software implementation. CGP equipped with the coevolution of adaptive size fitness predictors (coASFP) is used to design LID-classifiers working with fixed-point arithmetics with reduced precision, which is suitable for implementation in application-specific integrated circuits. In this particular task, we achieved a significant evolutionary design computational cost reduction in comparison with the original CGP. Moreover, coASFP effectively prevented overfitting in this task. Experiments with reduced precision LID-classifier design show that evolved classifiers working with 8-bit unsigned integer data representation, together with the input data scaling using the logical right shift, not only significantly outperformed hardware characteristics of all other investigated solutions but also achieved a better classifier accuracy in comparison with classifiers working with the floating-point numbers.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GA21-13001S" target="_blank" >GA21-13001S: Automated design of hardware accelerators for resource-aware machine learning</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Genetic Programming, 25th European Conference, EuroGP 2022

ISBN

978-3-031-02055-1

ISSN

—

e-ISSN

—

Number of pages

17

Pages from-to

85-101

Publisher name

Springer Nature Switzerland AG

Place of publication

Madrid

Event location

Madrid

Event date

Apr 20, 2022

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000873586200006