neural network and genetic algorithm

Question

I am working with a complex system, the system has five variables - depending upon values of these five variables, the response of the system is measured. There are seven output variables that are measured in-order to completely define the response.

I have been using artificial neural network to model relationship between the five variables and the seven output parameters. This has been successful so far.. The ANNs can predict really well the output (I have tested the trained network on a validation set of testcases also). I used python Keras/tensor flow for the same. BTW, I also tried the linear regression as function approximator but it produces large errors. These errors are expected considering that the system is highly non-linear and may not be continuous everywhere.

Now, I would like to predict the values of the five variables from a vector of the seven output parameters (target vector). Tried using Genetic algorithm for the same. After a lot of effort in designing the GA, I still end up getting high differences between target vector and the GA prediction. I just try to minimize the mean squared error between ANN prediction (function approximator) and target vector.

Is this the right approach to use ANN as function approximator and GA for design space exploration?

Answer 1

Yes, it is a good approach to do search space exploration using GA. But designing the crossover, mutation, generation evolution logic, etc. plays a major role in the determining the performance of the Genetic algo.

If your search space is limited, you can use exact methods (which solves to optimality).

There are few implementation in python-scipy itself

If you prefer to go with meta-heuristics,

there is a wide range of options other than Genetic algorithm

Memetic algorithm Tabu Search Simulated annealing Particle swarm optimization Ant colony optimization