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16

PPSN
2010
Springer

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General Lower Bounds for the Running Time of Evolutionary Algorithms

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General Lower Bounds for the Running Time of Evolutionary Algorithms

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Abstract. We present a new method for proving lower bounds in evolutionary computation based on ﬁtness-level arguments and an additional condition on transition probabilities between ﬁtness levels. The method yields exact or near-exact lower bounds for LO, OneMax, and all functions with a unique optimum. All lower bounds hold for every evolutionary algorithm that only uses standard mutation as variation operator.

Dirk Sudholt

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Distributed And Parallel Computing | Evolutionary Computation | Lower Bounds | Near-exact Lower Bounds | PPSN 2010 |

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Added	29 Jan 2011
Updated	29 Jan 2011
Type	Journal
Year	2010
Where	PPSN
Authors	Dirk Sudholt

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