Evolving mutation rates for the self-optimisation of genetic algorithms

A version of the standard genetic algorithm, in which the mutation rate is allowed to evolve freely, is applied across a set of optimisation problems, The resulting dynamics confirm the hypothesis that mutation rate, when allowed to evolve, will do so partly as a function of altitude in the fitness landscape. Further, it is demonstrated that this fact can be exploited in order to improve efficiency of the genetic algorithm when applied to a particular class of optimisation problem. Specifically, significant efficiency gains are established in those problems in which the fitness function is not stationary over time.