On the analysis of the (1+ 1) evolutionary algorithm
Theoretical Computer Science
A study of drift analysis for estimating computation time of evolutionary algorithms
Natural Computing: an international journal
On the effect of populations in evolutionary multi-objective optimization
Proceedings of the 8th 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
A Blend of Markov-Chain and Drift Analysis
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
On the impact of the mutation-selection balance on the runtime of evolutionary algorithms
Proceedings of the tenth ACM SIGEVO workshop on Foundations of genetic algorithms
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Drift analysis with tail bounds
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Simplified Drift Analysis for Proving Lower Bounds in Evolutionary Computation
Algorithmica - Special Issue: Theory of Evolutionary Computation
Theory of Randomized Search Heuristics: Foundations and Recent Developments
Theory of Randomized Search Heuristics: Foundations and Recent Developments
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Drift analysis is a powerful tool to prove upper and lower bounds on the runtime of randomized search heuristics. Its most famous application is a simple proof for the classical problem how the (1+1) Evolutionary Algorithm (EA) optimizes linear pseudo-Boolean functions. A relatively simple potential function allows to track the progress of the EA optimizing any linear function. In this work, we show that such beautiful proofs cease to exist if the mutation probability is slightly larger than the standard value of 1/n.