Multi-objective Evolutionary Algorithms are Generally Good: Maximizing
Monotone Submodular Functions over Sequences

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Multi-objective Evolutionary Algorithms are Generally Good: Maximizing Monotone Submodular Functions over Sequences

20 April 2021

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Papers citing "Multi-objective Evolutionary Algorithms are Generally Good: Maximizing Monotone Submodular Functions over Sequences"

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Title
Result Diversification by Multi-objective Evolutionary Algorithms with Theoretical Guarantees Chao Qian Danqin Liu Zhi Zhou 26 14 0 18 Oct 2021
Pareto Optimization for Subset Selection with Dynamic Partition Matroid Constraints A. Do Frank Neumann 44 8 0 16 Dec 2020
Maximizing Submodular or Monotone Functions under Partition Matroid Constraints by Multi-objective Evolutionary Algorithms A. Do Frank Neumann 43 9 0 23 Jun 2020
Multi-objective Evolutionary Algorithms are Still Good: Maximizing Monotone Approximately Submodular Minus Modular Functions Chao Qian 40 23 0 12 Oct 2019
Submodular Maximization Beyond Non-negativity: Guarantees, Fast Algorithms, and Applications Christopher Harshaw Moran Feldman Justin Ward Amin Karbasi 47 104 0 19 Apr 2019
Adaptive Sequence Submodularity Marko Mitrovic Ehsan Kazemi Moran Feldman Andreas Krause Amin Karbasi 36 27 0 15 Feb 2019
Pareto Optimization for Subset Selection with Dynamic Cost Constraints Vahid Roostapour Aneta Neumann Frank Neumann Tobias Friedrich 43 73 0 14 Nov 2018
Maximizing Submodular or Monotone Approximately Submodular Functions by Multi-objective Evolutionary Algorithms Chao Qian Yang Yu K. Tang Xin Yao Zhi Zhou 41 50 0 20 Nov 2017
Submodular meets Spectral: Greedy Algorithms for Subset Selection, Sparse Approximation and Dictionary Selection Abhimanyu Das David Kempe 202 484 0 19 Feb 2011
Computational Complexity Analysis of Simple Genetic Programming On Two Problems Modeling Isolated Program Semantics Greg Durrett Frank Neumann Una-May O’Reilly 105 51 0 27 Jul 2010