Abstract This article investigates the of applicability of adding evolvability promoting
mechanisms to a genetic algorithm to enhance its ability to handle perpetually novel
dynamic environments, especially one that has stationary periods allowing the Genetic
Algorithm (GA) to converge on a temporary global optimum.We utilize both biological and
evolutionary computation (EC) definitions of evolvability to create two measures: one based
on the improvements in fitness; the other based on the amount of genotypic change. These
two evolvability measures are used alongside fitness to modify how selection proceeds
in the GA. We call this modified GA the Estimation of Evolvability Genetic Algorithm
(EEGA). When tested against a regular GA (with random immigrants), the EEGA is able
to track the global optimum more closely than the GA during the dynamic period. Unlike
most GA extensions, the EEGA works effectively at a lower level of diversity than does the
GA, showing that it is the quality of the diverse members in the population and not just the
quantity that helps the GA evolve.
