Drift analysis and average time complexity of evolutionary algorithms
Artificial Intelligence
On the analysis of the (1+ 1) evolutionary algorithm
Theoretical Computer Science
Towards an analytic framework for analysing the computation time of evolutionary algorithms
Artificial Intelligence
A study of drift analysis for estimating computation time of evolutionary algorithms
Natural Computing: an international journal
Journal of Computer Science and Technology
On the Optimization of Monotone Polynomials by Simple Randomized Search Heuristics
Combinatorics, Probability and Computing
A Blend of Markov-Chain and Drift Analysis
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
On the brittleness of evolutionary algorithms
FOGA'07 Proceedings of the 9th international conference on Foundations of genetic algorithms
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
Runtime analysis of the (1+1) evolutionary algorithm on strings over finite alphabets
Proceedings of the 11th workshop proceedings on Foundations of genetic algorithms
A time complexity analysis of ACO for linear functions
SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
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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The proof of Theorem 6 in the paper by J. He and X. Yao [Artificial Intelligence 127 (1) (2001) 57-85] contains a mistake, although the theorem is correct [S. Droste et al., Theoret. Comput. Sci. 276 (2002) 51-81]. This note gives a revised proof and theorem. It turns out that the revised theorem is more general than the original one given an evolutionary algorithm with mutation probability pm = 1/(2n), using the same proof method as given by J. He and X. Yao [Artificial Intelligence 127 (1) (2001) 57-85].