How to take negative of the neural network function for maximization?

I have created a neural network in matlab using the nntool and now I using the trained neural network as a fitness function for my genetic algorithm to maximize the output of my function given 3 input variables. Optimization in matlab is by default used for minimization but as i want to maximize my function i understand that I need to take negative of the function but I am not able to take a negative of the neural network function as we do for normal functions.

Answer 1

[Hornett]

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.