The Runtime of the Compact Genetic Algorithm on Jump Functions
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In the first and so far only mathematical runtime analysis of an estimation-of-distribution algorithm (EDA) on a multimodal problem, Hasenöhrl and Sutton (GECCO 2018) showed for any k = o(n) that the compact genetic algorithm (cGA) with any hypothetical population size μ = Ω(ne^4k + n^3.5+ε) with high probability finds the optimum of the n-dimensional jump function with jump size k in time O(μ n^1.5log n). We significantly improve this result for small jump sizes k <1/20ln n -1. In this case, already for μ = Ω(√(n)log n) ∩poly(n) the runtime of the cGA with high probability is only O(μ√(n)). For the smallest admissible values of μ, our result gives a runtime of O(n log n), whereas the previous one only shows O(n^5+ε). Since it is known that the cGA with high probability needs at least Ω(μ√(n)) iterations to optimize the unimodal OneMx function, our result shows that the cGA in contrast to most classic evolutionary algorithms here is able to cross moderate-sized valleys of low fitness at no extra cost. For large k, we show that the exponential (in k) runtime guarantee of Hasenöhrl and Sutton is tight and cannot be improved, also not by using a smaller hypothetical population size. We prove that any choice of the hypothetical population size leads to a runtime that, with high probability, is at least exponential in the jump size k. This result might be the first non-trivial exponential lower bound for EDAs that holds for arbitrary parameter settings.
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