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Page Revision: 2012/06/10 18:51

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Technical Reports

• Yang Yu, Chao Qian, and Zhi-Hua Zhou. Towards Analyzing Crossover Operators in Evolutionary Search via General Markov Chain Switching Theorem. CORR abs/1111.0907, 2011.

Journal Articles

• Yang Yu, Xin Yao, and Zhi-Hua Zhou. On the approximation ability of evolutionary optimization with application to minimum set cover. Artificial Intelligence, 2012, 180-181:20-33. (PDF) (CORR abs/1011.4028)

• Yang Yu and Zhi-Hua Zhou. A new approach to estimating the expected first hitting time of evolutionary algorithms. Artificial Intelligence, 2008, 172(15): 1809-1832. (Extended from AAAI'06) (PDF)

Conference Papers

• Chao Qian, Yang Yu, and Zhi-Hua Zhou. On algorithm-dependent boundary case identification for problem classes. In: Proceedings of the 12th International Conference on Parallel Problem Solving from Nature (PPSN'12) Taormina, Italy, 2012. (PDF)

• Chao Qian, Yang Yu, and Zhi-Hua Zhou. An analysis on recombination in multi-objective evolutionary optimization. In: Proceedings of the 13th ACM Conference on Genetic and Evolutionary Computation (GECCO'11), Dublin, Ireland, 2011, pp. 2051-2058. (PDF) (This paper won the Best Paper Award of the Theory Track at GECCO'11)

• Chao Qian, Yang Yu, and Zhi-Hua Zhou. Collisions are helpful for computing unique input-output sequences. In: Proceedings of the 13th ACM Conference on Genetic and Evolutionary Computation (GECCO'11) (Companion Material/Poster), Dublin, Ireland, 2011, pp. 265-266. (PDF)

• Yang Yu, Chao Qian, and Zhi-Hua Zhou. Towards analyzing recombination operators in evolutionary search. In: Proceedings of the 11th International Conference on Parallel Problem Solving from Nature (PPSN'10) Part I, Krakow, Poland, 2010, pp.144-153. (PDF)

• Yang Yu and Zhi-Hua Zhou. On the usefulness of infeasible solutions in evolutionary search: A theoretical study. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC'08), Hong Kong, China, 2008, pp.835-840. (PDF)

• Yang Yu and Zhi-Hua Zhou. A new approach to estimating the expected first hitting time of evolutionary algorithms. In: Proceedings of the 21st National Conference on Artificial Intelligence (AAAI'06), Boston, MA, 2006, pp.555-560. (PDF)

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