Multi-objective evolutionary algorithms are generally good: Maximizing monotone submodular functions over sequences

Qian, Chao; Liu, Dan-Xuan; Feng, Chao; Tang, Ke

doi:10.1016/j.tcs.2022.12.011

Cited by 8 publications

(1 citation statement)

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“…The results from Theorems 1 and 2 are just two examples of a runtime result following an inductive sequence of improving steps based on constraint cost value. In the literature, there are further analyses of GSEMO following a similar approach (e. g., [14]), which we believe can be transferred to our sliding window approach to yield improved runtime guarantees.…”

Section: Definition 4 the Minimum Marginal Gain Of A Functionmentioning

confidence: 99%

Fast Pareto Optimization Using Sliding Window Selection

Neumann,

Witt

2023

Frontiers in Artificial Intelligence and Applications

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Pareto optimization using evolutionary multi-objective algorithms such as the classical GSEMO algorithm has been widely applied to solve constrained submodular optimization problems. A crucial factor determining the runtime of the used evolutionary algorithms to obtain good approximations is the population size of the algorithms which grows with the number of trade-offs that the algorithms encounter. In this paper, we introduce a sliding window speed up technique for recently introduced algorithms. We prove that our technique eliminates the population size as a crucial factor negatively impacting the runtime of the classical GSEMO algorithm and achieves the same theoretical performance guarantees as previous approaches within less computation time. Our experimental investigations for the classical maximum coverage problem confirms that our sliding window technique clearly leads to better results for a wide range of instances and constraint settings.

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Section: Definition 4 the Minimum Marginal Gain Of A Functionmentioning

confidence: 99%