Evolvability in dynamic fitness landscapes: a genetic algorithm approach

Evolvability refers to the adaptation of a population's genetic operator set over time. In traditional genetic algorithms, the genetic operator set, consisting of mutation operators, crossover operators, and their associated rates, is usually fixed. We explore the effects of allowing these operators and rates to vary under the influence of selection. The paper focuses on the suitability of alternative mutation models in dynamic landscapes. The mutation models include both traditional models in which all members of the population are subject to the same level of mutation and models in which mutation rates are genetically controlled.