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An analysis on recombination in multi-objective evolutionary optimization
Artificial Intelligence 204 (C):99-119 (2013)
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Citations of this work
Optimizing group learning: An evolutionary computing approach.Igor Douven - 2019 - Artificial Intelligence 275 (C):235-251.
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.
An analysis on recombination in multi-objective evolutionary optimization.Chao Qian, Yang Yu & Zhi-Hua Zhou - 2013 - Artificial Intelligence 204 (C):99-119.
Crossover can guarantee exponential speed-ups in evolutionary multi-objective optimisation.Duc-Cuong Dang, Andre Opris & Dirk Sudholt - 2024 - Artificial Intelligence 330 (C):104098.
Mathematical runtime analysis for the non-dominated sorting genetic algorithm II (NSGA-II).Weijie Zheng & Benjamin Doerr - 2023 - Artificial Intelligence 325 (C):104016.
References found in this work
An analysis on recombination in multi-objective evolutionary optimization.Chao Qian, Yang Yu & Zhi-Hua Zhou - 2013 - Artificial Intelligence 204 (C):99-119.
Drift analysis and average time complexity of evolutionary algorithms.Jun He & Xin Yao - 2001 - Artificial Intelligence 127 (1):57-85.
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.
A new approach to estimating the expected first hitting time of evolutionary algorithms.Yang Yu & Zhi-Hua Zhou - 2008 - Artificial Intelligence 172 (15):1809-1832.
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