To maximize the output of a neural network using a genetic algorithm (GA) in MATLAB, where optimization defaults to minimization, you can define a custom fitness function that negates the output of the neural network
Step 1: Define a Custom Fitness Function
Create a function that takes the inputs for your neural network, evaluates the network, and negates the output:
function negatedOutput = customFitnessFunction(inputs)
% Evaluate the neural network (assuming 'net' is your trained network)
nnOutput = net(inputs);
% Negate the output
negatedOutput = -nnOutput;
end
Step 2: Use the Custom Fitness Function in GA
Set up and run the genetic algorithm with your custom fitness function:
nVars = 3; % Number of input variables to the neural network
[x, fval] = ga(@customFitnessFunction, nVars);
This approach effectively transforms the maximization problem into a minimization problem suitable for MATLAB's GA function, allowing you to maximize the output of your neural network.
