Evolutionary Design of Reduced Precision Preprocessor for Levodopa-Induced Dyskinesia Classifier

• https://link.springer.com/chapter/10.1007/978-3-031-14714-2_34

Cartesian genetic programming, compositional coevolution, adaptive size fitness
predictors, levodopa-induced dyskinesia, approximate magnitude, energy-efficient

The aim of this work is to design a hardware-efficient implementation of data
preprocessing in the task of levodopa-induced dyskinesia classification. In this
task, there are three approaches implemented and compared: 1) evolution of
magnitude approximation using Cartesian genetic programming, 2) design of
preprocessing unit using two-population coevolution (2P-CoEA) of cartesian
programs and fitness predictors, which are small subsets of training set, and 3)
a design using three-population coevolution (3P-CoEA) combining compositional
coevolution of preprocessor and classifier with coevolution of fitness
predictors. Experimental results show that all of the three investigated
approaches are capable of producing energy-saving solutions, suitable for
implementation in hardware unit, with a quality comparable to baseline software
implementation. Design of approximate magnitude leads to correctly working
solutions, however, more energy-demanding than other investigated approaches.
3P-CoEA is capable of designing both preprocessor and classifier compositionally
while achieving smaller solutions than the design of approximate magnitude.
Presented 2P-CoEA results in the smallest and the most energy-efficient solutions
along with producing a solution with significantly better classification quality
for one part of test data in comparison with the software implementation.