construct NaiveBayes with the explicitly specified parameters
ncls - number of class labels
nftr - number of features
nval - number of values per each feature (assuming nominal - discrete finite-valued - features)
cpriors - prior probability distribution over class labels
m - equivalent sample size
ppriors - prior estimates of the probabilities P(f|C) (used for Bayesian parameter estimation
with equivalent sample size method)
perform linear normalization of the raw fitness values
also pre-compute the summed fitness values for roulette wheel selection
where the normalizedFitness of the best population member is
equal to 2 times the average fitness