Optimizing Monotone Functions Can Be Difficult
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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/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 Θ(n n) iterations. For c=1, we can still prove an upper bound of O(n^3/2). 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^Ω(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.
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