Adaptive threshold parameter estimation with recursive differential grouping for problem decomposition

Yuan Sun, Michael Kirley, Mohammad Nabi Omidvar, Xiaodong Li

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

14 Citations (Scopus)


Problem decomposition plays an essential role in the success of cooperative co-evolution (CC), when used for solving large-scale optimization problems. The recently proposed recursive differential grouping (RDG) method has been shown to be very efficient, especially in terms of time complexity. However, it requires an appropriate parameter setting to estimate a threshold value in order to determine if two subsets of decision variables interact or not. Furthermore, using one global threshold value may be insufficient to identify variable interactions in components with different contribution to the fitness value. Inspired by the different grouping 2 (DG2) method, in this paper, we adaptively estimates a threshold value based on computational round-off errors for RDG. We derive an upper bound of the round-off errors, which is shown to be sufficient when identifying variable interactions across a wide range of large-scale benchmark problems. Comprehensive numerical experimental results showed that the proposed RDG2 method achieved higher decomposition accuracy than RDG and DG2. When embedded into a CC framework, it achieved statistically equal or significantly better solution quality than RDG and DG2, when used to solve the benchmark problems.

Original languageEnglish
Title of host publicationProceedings of the 2018 Genetic and Evolutionary Computation Conference
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Number of pages8
ISBN (Electronic)9781450356183
Publication statusPublished - 2 Jul 2018
Externally publishedYes
EventThe Genetic and Evolutionary Computation Conference, 2018 - Kyoto, Japan
Duration: 15 Jul 201819 Jul 2018


ConferenceThe Genetic and Evolutionary Computation Conference, 2018
Abbreviated titleGECCO 2018
OtherThe Genetic and Evolutionary Computation Conference (GECCO) presents the latest high-quality results in genetic and evolutionary computation since 1999. Topics include: genetic algorithms, genetic programming, ant colony optimization and swarm intelligence, complex systems (artificial life/robotics/evolvable hardware/generative and developmental systems/artificial immune systems), digital entertainment technologies and arts, evolutionary combinatorial optimization and metaheuristics, evolutionary machine learning, evolutionary multiobjective optimization, evolutionary numerical optimization, real world applications, search-based software engineering, theory and more.
Internet address


  • Cooperative co-evolution
  • Large-scale continuous optimization
  • Parameter adaptation
  • Problem decomposition
  • Round-off error analysis

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