To develop a fitness function from an Artificial Neural Network (ANN) with two input variables and one output variable, you'll need to follow these general steps:
- Define the ANN architecture.
- Train the ANN
- Implement the Fitness Function
Here's an example of how you might implement this in MATLAB:
% Assuming you have a trained ANN named 'trainedNet'
% Define the fitness function
function fitness = myFitnessFunction(inputs)
% 'inputs' should be a 2-element vector corresponding to the two input variables
% Pass the inputs through the trained ANN
output = predict(trainedNet, inputs);
% Define your fitness evaluation criteria based on the ANN output
% For example, if you are trying to minimize the output:
fitness = -output; % Use negative sign if lower output is better
% If there are other criteria for the fitness, incorporate them here
end
% Example usage of the fitness function
inputExample = [input1, input2]; % Replace with actual input values
fitnessValue = myFitnessFunction(inputExample);
In this example, the myFitnessFunction function takes a vector of inputs, uses the predict function to obtain the output from the trained ANN, and then calculates the fitness value based on that output. The way you calculate the fitness will depend on the specific goals of your optimization problem. If you're trying to minimize the ANN's output, you might use a negative sign as shown above. If you're trying to maximize it, you'd use the output directly or apply some transformation that aligns with your optimization objectives.
