The paper extends the average-gain drift model to a k-step multiple-gain bound and applies it to obtain expected first hitting time upper bounds for (mu+lambda) EA on three combinatorial problems.
Runtime analysis of the (1+ 1) evolutionary algorithm for the chance-constrained knapsack problem,
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Running-time Analysis of ($\mu+\lambda$) Evolutionary Combinatorial Optimization Based on Multiple-gain Estimation
The paper extends the average-gain drift model to a k-step multiple-gain bound and applies it to obtain expected first hitting time upper bounds for (mu+lambda) EA on three combinatorial problems.