Cooperative Co-evolution with online optimizer selection for large-scale optimization

Yuan Sun, Michael Kirley, Xiaodong Li

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

9 Citations (Scopus)


Cooperative co-evolution (CC) is an effective framework that can be used to solve large-scale optimization problems. It typically divides a problem into components and uses one optimizer to solve the components in a round-robin fashion. However the relative contribution of each component to the overall fitness value may vary. Furthermore, using one optimizer may not be sufficient when solving a wide range of components with different characteristics. In this paper, we propose a novel CC framework which can select an appropriate optimizer to solve a component based on its contribution to the fitness improvement. In each evolutionary cycle, the candidate optimizer and component that make the greatest contribution to the fitness improvement are selected for evolving. We evaluated the efficacy of the proposed CC with Optimizer Selection (CCOS) algorithm using large-scale benchmark problems. The numerical experiments showed that CCOS outperformed the CC model without optimizer selection ability. When compared against several other state-of-the-art algorithms, CCOS generated competitive solution quality.

Original languageEnglish
Title of host publicationProceedings of the 2018 Genetic and Evolutionary Computation Conference
Subtitle of host publication2018 Genetic and Evolutionary Computation Conference, GECCO 2018; Kyoto; Japan; 15 July 2018 through 19 July 2018
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
Conference number: 20th (Proceedings)


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


  • Algorithm hybridization
  • Algorithm selection
  • Cooperarive co-evolution
  • Large-scale optimization
  • Resources allocation

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