I have a set of observations of real data, and a set of Random Variables I produce my self. The goal is to generated Random Variables with the same Distribution as the real data. To investigate the simliarity the two distributions, I use the Kullback-Leibler Divergence. Now with the genetic algorithm of Matlab, I try to find the best parameters for my generator, the thing only is that genetic algorithm is stochastic and so I have no consistent results for the Kullback Leibler Divergence. One idea would be though to run my generator say a thousand times then chosse the minimum Kullback-Leibler Divergence and reproduce the result with this: http://www.mathworks.ch/ch/help/gads/reproducing-your-results-1.htmlWould that work? The idea would be to get a continuous fitness function...
Stochasticity of Genetic Algorithm
genetic algorithms