Corpus ID: 204512428

Multi-objective Evolutionary Algorithms are Still Good: Maximizing Monotone Approximately Submodular Minus Modular Functions

@article{Qian2019MultiobjectiveEA,
  title={Multi-objective Evolutionary Algorithms are Still Good: Maximizing Monotone Approximately Submodular Minus Modular Functions},
  author={Chao Qian},
  journal={ArXiv},
  year={2019},
  volume={abs/1910.05492}
}
  • Chao Qian
  • Published in ArXiv 2019
  • Computer Science
  • As evolutionary algorithms (EAs) are general-purpose optimization algorithms, recent theoretical studies have tried to analyze their performance for solving general problem classes, with the goal of providing a general theoretical explanation of the behavior of EAs. Particularly, a simple multi-objective EA, i.e., GSEMO, has been shown to be able to achieve good polynomial-time approximation guarantees for submodular optimization, where the objective function is only required to satisfy some… CONTINUE READING

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