I'm using the genetic algorithm to select the architecture of a neural network, however it is yet to provide good results.
I have an initial population size of 40 (each individual represents a possible architecture, made up of two genes, represented by integers), and I have evolved them for 10 generations.
The mean fitness of the individuals in the generation improves for the first 3 generations, then plateaus at a value slightly above the fitness of the best individual. The fitness of the best individual stays the same over all 10 generations. In maths terms, I think this is known as being stuck in a local minimum.
I find it very unlikely that I found the best solution in my first generation, so something seems to be stopping the individuals from evolving into anything better than this. Is there anything I can do to try and make the the fitness of the best individual improve after each generation?