The estimation of evolvability genetic algorithm

In this paper we utilize both the biological and common EC definitions of evolvability to create two measures: one based on fitness improvement, the other based on the amount of genotypic change. The evolvability measures are then used to increase the exploratory behavior of the GA to escape from local optima and track moving environments. The estimation of evolvability genetic algorithm was successfully tested against the GA both in stationary and dynamic environments. The EEGA behaved so well that it was difficult to determine solely from the behavior of the EEGA when the function began moving. Furthermore, unlike most GA extensions created for dynamic environment, the EEGA actually performs at a lower diversity level than a standard GA.