How to use the genetic algorithm to optimize "trainingOptions" for trainNetwork?

Hi all, I am trying to use the genetic algorithm optimizer to tune hyperparameters in a neural network made using the deep network designer. When setting up the objective function, I can't seem the make the InitialLearnRate one of the hyperparameters to optimise. As far as I understand, the trainingOptions function requries the learn rate to be a postive integer, but how would I set it up if I want the genetic algorithm to vary that? I have attached the section of code, any help will be greatly appreciated!

function f = SeqFunction(p)

% Hyperparams for the optimization 
numHiddenUnits = round(p(1));
LearnRate = round(p(2));

%Define the network
numFeatures = 7;
numResponses = 1;

layers = [...
    sequenceInputLayer(numFeatures)
    lstmLayer(numHiddenUnits, 'OutputMode',"sequence")
    dropoutLayer(0.2)
    lstmLayer(numHiddenUnits, 'OutputMode',"sequence")
    %dropoutLayer(0.2)
    fullyConnectedLayer(numResponses)
    regressionLayer];

options = trainingOptions('adam', ...
    'MaxEpochs',100, ...
    'GradientThreshold',1, ...
    'InitialLearnRate',LearnRate, ... %This is the line I am struggling with
    'Shuffle', 'never', ...
    'LearnRateSchedule','piecewise', ...
    'LearnRateDropPeriod',125, ...
    'LearnRateDropFactor',0.9, ...
    'Verbose',0, ...
    'Plots','training-progress');

% Train the network
net = trainNetwork(XTrain,YTrain,layers,options);