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Drift analysis and average time complexity of evolutionary algorithms
Artificial Intelligence 127 (1):57-85 (2001)
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Citations of this work
Maximizing submodular or monotone approximately submodular functions by multi-objective evolutionary algorithms.Chao Qian, Yang Yu, Ke Tang, Xin Yao & Zhi-Hua Zhou - 2019 - Artificial Intelligence 275 (C):279-294.
When move acceptance selection hyper-heuristics outperform Metropolis and elitist evolutionary algorithms and when not.Andrei Lissovoi, Pietro S. Oliveto & John Alasdair Warwicker - 2023 - Artificial Intelligence 314 (C):103804.
An analysis on recombination in multi-objective evolutionary optimization.Chao Qian, Yang Yu & Zhi-Hua Zhou - 2013 - Artificial Intelligence 204 (C):99-119.
Towards an analytic framework for analysing the computation time of evolutionary algorithms.Jun He & Xin Yao - 2003 - Artificial Intelligence 145 (1-2):59-97.
On the approximation ability of evolutionary optimization with application to minimum set cover.Yang Yu, Xin Yao & Zhi-Hua Zhou - 2012 - Artificial Intelligence 180-181 (C):20-33.
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