Problem with wavelet code

Dear MATLAB users

I have the following code which is used to select limited number of point from a vector and calclate the wavelet of the original vector and the new-sampled vector from the points we selected then calculate the wavelet energy ratio between the two vectors:

% Adjustable parameters
frequency = 58;          % Frequency of the sine function
speed_rpm = 6000;        % Rotation speed in rpm
duration = 60 / speed_rpm;  % Duration of the signal for both high and low sampling rates
sampling_rate_high = 1668;   % High sampling rate
num_samples_low = 16;      % Number of samples for signal_low

% Time vector for signal_high
t_high = linspace(0, duration, duration * sampling_rate_high);

% Generate sine function for signal_high
signal_high = sin(2 * pi * frequency * t_high);

% Calculate the average of signal_high
average_signal_high = mean(signal_high);

% Select a subset of values from signal_high
step = floor(length(signal_high) / num_samples_low);
selected_indices = 1:step:length(signal_high);
selected_values = signal_high(selected_indices);

% Normalize selected subset to match the average of signal_high
average_selected_values = mean(selected_values);
normalized_selected_values = selected_values - (average_selected_values - average_signal_high);

% Linear interpolation to match the length of t_low
t_low = linspace(0, duration, num_samples_low);
normalized_selected_values_interp = interp1(linspace(0, duration, length(normalized_selected_values)), normalized_selected_values, t_low, 'linear', 'extrap');

% Construct signal_low
signal_low = normalized_selected_values_interp;

% Compute wavelet transform for both high and low sampling rates
scales = 1:64; % Choose appropriate scales for wavelet analysis
coefficients_high = cwt(signal_high, scales, 'db4');
coefficients_low = cwt(signal_low, scales, 'db4');

% Calculate the wavelet energy ratio
energy_ratio = zeros(1, length(scales));
for i = 1:length(scales)
    energy_ratio(i) = sum(abs(coefficients_low(i, :)).^2) / sum(abs(coefficients_high(i, :)).^2);
end

% Plotting
figure;

% Original signal (signal_high)
subplot(3, 2, 1);
plot(t_high, signal_high, 'b');
title('Original Signal (High Sampling Rate)');
xlabel('Time');
ylabel('Amplitude');

% Signal_low
subplot(3, 2, 2);
stem(selected_indices, selected_values, 'r', 'Marker', 'o');
hold on;
plot(t_low, signal_low, 'b');
title('Signal Low Sampling Rate');
xlabel('Index');
ylabel('Amplitude');
legend('Selected Values', 'Interpolated Signal');

% Wavelet transform for signal_high
subplot(3, 2, 3);
imagesc(t_high, scales, abs(coefficients_high));
colorbar;
title('Wavelet Transform (High Sampling Rate)');
xlabel('Time');
ylabel('Scale');

% Wavelet transform for signal_low
subplot(3, 2, 4);
imagesc(t_low, scales, abs(coefficients_low));
colorbar;
title('Wavelet Transform (Low Sampling Rate)');
xlabel('Time');
ylabel('Scale');

% Energy ratio as a function of scale
subplot(3, 2, [5 6]);
plot(scales, energy_ratio, 'LineWidth', 1.5);
title('Wavelet Energy Ratio');
xlabel('Scale');
ylabel('Energy Ratio');

% Display the average energy ratio
disp(['Average Wavelet Energy Ratio: ', num2str(mean(energy_ratio))]);

I am facing a problem, I am playing with the adjustable paramters but when I put the sampling rate lower than 1668 the code stop to work:

also when I change the speed this affect the number of samples, I know I missing something but I have no idea about it.

speed_rpm = 6000;        % Rotation speed in rpm
duration = 60 / speed_rpm;  % Duration of the signal for both high and low sampling rates
sampling_rate_high = 1668;   % High sampling rate
num_samples_low = 16;      % Number of samples for signal_low

the second part of my question:

I would like to implement a code that reduce the number of samples taken into consideration and define the appropriate positions (unifrom or non uniform arraangement) without losing the information of the original signal. for this I am using the wavelet energy ratio as an indicator but to compute all the possible arrangement it would be computationally cost so I am thinking about integrating Genetic Algorithms GA. to make it but I have no idea about it, I know just the theory of GA so if someone can set me on the path I will be very greatful for it.

Thanks a lot for reading and answering :) .