Investigating Overlapped Strategies to Solve Overlapping Problems in a Cooperative Co-evolutionary Framework

Julien Blanchard, Timoteo Carletti, Charlotte Beauthier

Research output: Contribution in Book/Catalog/Report/Conference proceedingChapter (peer-reviewed)peer-review


Cooperative co-evolution is recognized as an effective approach for solving large-scale optimization problems. It breaks down the problem dimensionality by splitting a large-scale problem into ones focusing on a smaller number of variables. This approach is successful when the studied problem is decomposable. However, many practical optimization problems can not be split into disjoint components. Most of them can be seen as interconnected components that share some variables with other ones. Such problems composed of parts that overlap each other are called overlapping problems. This paper proposes a modified cooperative co-evolutionary framework allowing to deal with non-disjoint subproblems in order to decompose and optimize overlapping problems efficiently. The proposed algorithm performs a new decomposition based on differential grouping to detect overlapping variables. A new cooperation strategy is also introduced to manage variables shared among several components. The performance of the new overlapped framework is assessed on large-scale overlapping benchmark problems derived from the CEC’2013 benchmark suite and compared with a state-of-the-art non-overlapped framework designed to tackle overlapping problems.

Original languageEnglish
Title of host publicationOptimization and Learning - 4th International Conference, OLA 2021, Proceedings
Subtitle of host publication4th International Conference, OLA 2021, Catania, Italy, June 21-23, 2021, Proceedings
EditorsBernabé Dorronsoro, Patricia Ruiz, Lionel Amodeo, Mario Pavone
Number of pages13
ISBN (Electronic)978-3-030-85672-4
Publication statusPublished - 17 Aug 2021

Publication series

NameCommunications in Computer and Information Science
ISSN (Print)1865-0929


  • Cooperative co-evolution
  • Evolutionary algorithms
  • Large-scale global optimization
  • Overlapping problem


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