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
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
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Optimizing monotone functions can be difficult
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
Optimizing monotone functions can be difficult
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
Non-uniform mutation rates for problems with unknown solution lengths
Proceedings of the 11th workshop proceedings on Foundations of genetic algorithms
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Proceedings of the 14th 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 show that the (1+1) evolutionary algorithm using an arbitrary mutation rate p = c/n, c a constant, finds the optimum of any n-bit pseudo-Boolean linear function f in expected time Θ(n log n). Since previous work shows that universal drift functions cannot exist for c larger than a certain constant, we define drift functions depending on p and f. This seems to be the first time in the theory of evolutionary algorithms that drift functions are used that take into account the particular problem instance.