Optimizing Monotone Functions Can Be Difficult

Extending previous analyses on function classes like linear functions, we analyze how the simple (1+1) evolutionary algorithm optimizes pseudo-Boolean functions that are strictly monotone. Contrary to what one would expect, not all of these functions are easy to optimize. The choice of the constant $c$ in the mutation probability $p(n)=c\mathrm{/}n$ can make a decisive difference. We show that if $c<1$, then the (1+1) evolutionary algorithm finds the optimum of every such function in $\mathrm{\Theta }(n\log n)$ iterations. For $c=1$, we can still prove an upper bound of $O(n^{3\mathrm{/}2})$. However, for $c>33$, we present a strictly monotone function such that the (1+1) evolutionary algorithm with overwhelming probability does not find the optimum within $2^{\mathrm{\Omega }(n)}$ iterations. This is the first time that we observe that a constant factor change of the mutation probability changes the run-time by more than constant factors.