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Maximizing Submodular Functions under Matroid Constraints by Multi-objective Evolutionary Algorithms
[chapter]
2014
Lecture Notes in Computer Science
Many combinatorial optimization problems have underlying goal functions that are submodular. The classical goal is to find a good solution for a given submodular function f under a given set of constraints. In this paper, we investigate the runtime of a multi-objective evolutionary algorithm called GSEMO until it has obtained a good approximation for submodular functions. For the case of monotone submodular functions and uniform cardinality constraints we show that GSEMO achieves a (1 −
doi:10.1007/978-3-319-10762-2_91
fatcat:l4na2rgmovcphl2aqnatrz3ko4