Dynamic Mutation Based Pareto Optimization for Subset Selection

  • Mengxi WuEmail author
  • Chao Qian
  • Ke Tang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10956)


Subset selection that selects the best k variables from n variables is a fundamental problem in many areas. Pareto optimization for subset selection (called POSS) is a recently proposed approach for subset selection based on Pareto optimization and has shown good approximation performances. In the reproduction of POSS, it uses a fixed mutation rate, which may make POSS get trapped in local optimum. In this paper, we propose a new version of POSS by using a dynamic mutation rate, briefly called DM-POSS. We prove that DM-POSS can achieve the best known approximation guarantee for the application of sparse regression in polynomial time and show that DM-POSS can also empirically perform well.


Subset selection Pareto optimization Sparse regression Dynamic mutation 


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© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Anhui Province Key Lab of Big Data Analysis and ApplicationUniversity of Science and Technology of ChinaHefeiChina
  2. 2.Shenzhen Key Lab of Computational IntelligenceSouthern University of Science and TechnologyShenzhenChina

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