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    Chapter and Conference Paper

    Selection Hyper-heuristics Can Provably Be Helpful in Evolutionary Multi-objective Optimization

    Selection hyper-heuristics are automated methodologies for selecting existing low-level heuristics to solve hard computational problems. They have been found very useful for evolutionary algorithms when solvin...

    Chao Qian, Ke Tang, Zhi-Hua Zhou in Parallel Problem Solving from Nature – PPSN XIV (2016)

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    Chapter and Conference Paper

    On the Effectiveness of Sampling for Evolutionary Optimization in Noisy Environments

    Sampling has been often employed by evolutionary algorithms to cope with noise when solving noisy real-world optimization problems. It can improve the estimation accuracy by averaging over a number of samples,...

    Chao Qian, Yang Yu, Yaochu **, Zhi-Hua Zhou in Parallel Problem Solving from Nature – PPS… (2014)

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    Chapter and Conference Paper

    On Algorithm-Dependent Boundary Case Identification for Problem Classes

    Running time analysis of metaheuristic search algorithms has attracted a lot of attention. When studying a metaheuristic algorithm over a problem class, a natural question is what are the easiest and the harde...

    Chao Qian, Yang Yu, Zhi-Hua Zhou in Parallel Problem Solving from Nature - PPSN XII (2012)

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    Chapter and Conference Paper

    Unlabeled Data and Multiple Views

    In many real-world applications there are usually abundant unlabeled data but the amount of labeled training examples are often limited, since labeling the data requires extensive human effort and expertise. T...

    Zhi-Hua Zhou in Partially Supervised Learning (2012)

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    Chapter and Conference Paper

    Towards Analyzing Recombination Operators in Evolutionary Search

    Recombination (also called crossover) operators are widely used in EAs to generate offspring solutions. Although the usefulness of recombination has been well recognized, theoretical analysis on recombination ope...

    Yang Yu, Chao Qian, Zhi-Hua Zhou in Parallel Problem Solving from Nature, PPSN XI (2010)