On the genetic algorithm with adaptive mutation rate and selected statistical applications
We give sufficient conditions which the mutation rate must satisfy for the convergence of the genetic algorithm when that rate is allowed to change throughout iterations. The empirical performance of the algorithm with regards to changes in the mutation parameter is explored via test functions, ARIMA model selection and maximum likelihood estimation illustrating the advantages of letting the mutation rate decrease from rather unusual high values to the commonly used low ones.
model selection maximum likelihood markov chains evolutionary algorithms time series