Drift analysis and average time complexity of evolutionary algorithms
Artificial Intelligence
Evolutionary Algorithms and the Maximum Matching Problem
STACS '03 Proceedings of the 20th Annual Symposium on Theoretical Aspects of Computer Science
A study of drift analysis for estimating computation time of evolutionary algorithms
Natural Computing: an international journal
Rigorous hitting times for binary mutations
Evolutionary Computation
Theoretical analysis of diversity mechanisms for global exploration
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Rigorous analyses of fitness-proportional selection for optimizing linear functions
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Multiobjectivization by Decomposition of Scalar Cost Functions
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
ICARIS '09 Proceedings of the 8th International Conference on Artificial Immune Systems
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
General lower bounds for the running time of evolutionary algorithms
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Drift analysis with tail bounds
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Analyzing different variants of immune inspired somatic contiguous hypermutations
Theoretical Computer Science
On the effect of populations in evolutionary multi-objective optimisation**
Evolutionary Computation
Free lunches on the discrete Lipschitz class
Theoretical Computer Science
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Drift analysis is a powerful tool used to bound the optimization time of evolutionary algorithms (EAs). Various previous works apply a drift theorem going back to Hajek in order to show exponential lower bounds on the optimization time of EAs. However, this drift theorem is tedious to read and to apply since it requires two bounds on the moment-generating (exponential) function of the drift. A recent work identifies a specialization of this drift theorem that is much easier to apply. Nevertheless, it is not as simple and not as general as possible. The present paper picks up Hajek's line of thought to prove a drift theorem that is very easy to use in evolutionary computation. Only two conditions have to be verified, one of which holds for virtually all EAs with standard mutation. The other condition is a bound on what is really relevant, the drift. Applications show how previous analyses involving the complicated theorem can be redone in a much simpler and clearer way. Therefore, the simplified theorem is also a didactical contribution to the runtime analysis of EAs.