Comparing genetic robustness in generational vs. steady state evolutionary algorithms

Previous research has shown that evolutionary systems not only try to develop solutions that satisfy a fitness requirement, but indirectly attempt to develop genetically robust solutions as well -solutions where average loss of fitness due to crossover and other genetic variation operators is minimized. It has been shown that in a simple "two peaks" problem, where the fitness landscape consists of a broad, low peak, and a narrow, high peak, individuals initially converge on the lower (less fit), but broader peak, and that increasing an individual's genetic robustness through growth is a necessary prerequisite for convergence on the higher, narrower peak 18. If growth is restricted, the population remains converged on the less fit solution. We tested whether this result holds true only for generational algorithms, or whether it applies to steady state algorithms as well. We conclude that although growth occurs with both algorithms, the steady state algorithm is able to converge on the higher peak without this growth. This result shows that the role of genetic robustness in the evolutionary process is significantly different in generational versus steady state algorithms.