Please give me a hand solution for the design

HEURISTIC MINIMIZATION, GENETIC ALGORITHM DESIGN:

${?}^{***}$ Denoise EEG Signals Using Deep Learning Regression with GPU Acceleration. This example shows how to remove electro$-$oculogram $($EOG$)$ noise from electroencephalogram $($EEG$)$ signals using the EEGdenoiseNet benchmark dataset $[1]$ and deep learning regression. The EEGdenoiseNet dataset contains $4514$ clean EEG segments and $3400$ ocular artifact segments that can be used to synthesize noisy EEG segments with the ground$-$truth clean EEG $($the dataset also contains muscular artifact segments, but these will not be used in this example$).$

This example uses clean and EOG$-$contaminated EEG signals to train a long short$-$term memory $($LSTM$)$ model to remove the EOG artifacts. The regression model was trained with raw input signals and with signals transformed by the short$-$time Fourier transform $($STFT$).$ The STFT model improves performance especially at degraded SNR values.

To enable GPU acceleration for STFT computations, you must have

Please give me a hand solution for the design

Parallel Computing Toolbox ${?}^{Tw.}.$ To see which GPUs are supported, see GPU Computing Requirements $($Parallel Combutine Toolbox$)."$

Design a GA algorithm to find the minimum of the EEG with the colored EOG artifact noise. Show your design steps and hand calculations $(7$ marks$).$

Write a GA using $($python$,c{t}^{+},$ or mat$/$ab$)$ to perform this heuristic minimization. $(3$ marks$).$