Before revision: We use an elementary argument building on group actions to prove that the selection-free steady state genetic algorithm analyzed by Sutton and Witt (GECCO 2019) takes an expected number of \Omega(2^n / \sqrt n) iterations to find any particular target search point , regardless of the population size \mu. This improves over the previous best lower bound of \Omega(\exp(n^{\delta/2})) valid for population sizes \mu = O(n^{1/2 - \delta}), 0 < \delta < 1/2.
After revision: We use an elementary argument building on group actions to prove that the selection-free steady state genetic algorithm analyzed by Sutton and Witt (GECCO 2019) takes an expected number of \Omega(2^n / \sqrt n) iterations to find any particular target search point . This bound is valid for all population sizes \mu. Our result improves over the previous best lower bound of \Omega(\exp(n^{\delta/2})) valid for population sizes \mu = O(n^{1/2 - \delta}), 0 < \delta < 1/2.