An individually variable mutation-rate strategy for genetic algorithms

In Neo-Darwinism, mutation can be considered to be unaffected by selection pressure. This is the metaphor generally used by the genetic algorithm for its treatment of the mutation operation, which is usually regarded as a background operator. This metaphor, however, does not take into account the fact that mutation has been shown to be affected by external events. In this paper, we propose a mutation-rate strategy that is variable between individuals within a given generation based on the individual's relative performance for the purpose of function optimization.