Drift analysis and average time complexity of evolutionary algorithms
Artificial Intelligence
On the analysis of the (1+ 1) evolutionary algorithm
Theoretical Computer Science
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
On the effect of populations in evolutionary multi-objective optimization
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Randomized local search, evolutionary algorithms, and the minimum spanning tree problem
Theoretical Computer Science
Evolutionary algorithms and matroid optimization problems
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Adjacency list matchings: an ideal genotype for cycle covers
Proceedings of the 9th 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
Simplified Drift Analysis for Proving Lower Bounds in Evolutionary Computation
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Computing single source shortest paths using single-objective fitness
Proceedings of the tenth ACM SIGEVO workshop on Foundations of genetic algorithms
Theoretical analysis of fitness-proportional selection: landscapes and efficiency
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Edge-based representation beats vertex-based representation in shortest path problems
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Proceedings of the 12th annual conference on Genetic and evolutionary computation
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Speeding up evolutionary algorithms through restricted mutation operators
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Tight analysis of the (1+1)-ea for the single source shortest path problem
Evolutionary Computation
Revisiting the restricted growth function genetic algorithm for grouping problems
Evolutionary Computation
Non-existence of linear universal drift functions
Theoretical Computer Science
The use of tail inequalities on the probable computational time of randomized search heuristics
Theoretical Computer Science
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
ACO beats EA on a dynamic pseudo-boolean function
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
When do evolutionary algorithms optimize separable functions in parallel?
Proceedings of the twelfth workshop on Foundations of genetic algorithms XII
A method to derive fixed budget results from expected optimisation times
Proceedings of the 15th annual conference on Genetic and evolutionary computation
How the (1+λ) evolutionary algorithm optimizes linear functions
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Mutation rate matters even when optimizing monotonic functions
Evolutionary Computation
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We give a simple and short alternative proof of the multiplicative drift theorem published recently (Doerr, Johannsen, Winzen (GECCO 2010)). It completely avoids the use of drift theorems previously used in the theory of evolutionary computation. By this, its proof is fully self-contained. The new theorem yields exactly the same bounds for expected runtimes as the previous theorem. In addition, it also gives good bounds on the deviations from the mean. This shows, for the first time, that the classical O(n log n) run-time bound for the (1+1) evolutionary algorithm for optimizing linear functions holds with high probability (and not just in expectation). Similar improvements are obtained for other classical problems in the evolutionary algorithms literature, for example computing minimum spanning trees, finding single-source shortest paths, and finding Eulerian cycles.