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Stochastic Population Update Can Provably Be Helpful in Multi-Objective Evolutionary Algorithms
This work is an attempt to challenge a common practice in the design of existing MOEAs.
Robust Subset Selection by Greedy and Evolutionary Pareto Optimization
We first show that the greedy algorithm can obtain an approximation ratio of $1-e^{-\beta\gamma}$, where $\beta$ and $\gamma$ are the correlation and submodularity ratios of the objective functions, respectively; and then propose EPORSS, an evolutionary Pareto optimization algorithm that can utilize more time to find better subsets.
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