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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...
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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,...
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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...
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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...
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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...