MATLAB: How to use Genetic Algoritm to predict future cost? I am stuck and would like some guidance to the next step

ant colony optimizationgenetic algorithm

% Import & initialize gold project data
% from file:
% Training_gold.xlsx
T = readtable ("Testing gold (23-7-2019).xlsx");
% Specify data range used as input
DataRange = "B5:J14";
% Specify column names and types
VariableNames = ["gold", "shcap", "shunit", "truckcap", "truckunit", "eqltime", "midevtime", "miprod", "capex"];
SelectedVariableNames = ["gold", "shcap", "shunit", "truckcap", "truckunit", "eqltime", "midevtime", "miprod", "capex"];
VariableTypes = ["double", "double", "double", "double", "double", "double", "double", "double", "double"];
% Create the variables
goldprice = ('gold');
shovelcap = ('shcap');
shovelunit = ('shunit');
truckcap = ('truckcap');
truckunit = ('truckunit');
eqltime = ('eqltime');
midevtime = ('minedevtime');
miprod = ('miprod');
capex = ('capex');
%Save the variables into one *.mat file
save Training_gold_1 goldprice shovelcap shovelunit truckcap truckunit eqltime midevtime miprod capex 
%Clear them out of the workspace
clear goldprice shovelcap shovelunit truckcap truckunit eqltime midevtime miprod capex
%Load the cost variables
load ("Training_gold_1.mat");

Best Answer

The ga (Genetic Algorithm) function is an optimisation function. It will provide the best parameter estimates for the mathematical model (apparently for gold prices) you want it to optimise based on historic data. You can then use your model with those estimated parameters to predict the future cost.
If you have an example using ga to predict anything in any other context other than a fitted model, please share it.
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