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

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Part of the Communications in Computer and Information Science book series (CCIS,volume 1443)


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.


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

The present research benefited from computational resources made available on the Tier-1 supercomputer of the Fedération Wallonie-Bruxelles, infrastructure funded by the Walloon Region under the grant agreement n\(^{\circ }\)1117545.

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  1. 1.

    Note that the function \(f_{13}\) in [17] also contains overlapping subcomponents but it has not been included in the benchmark set because their overlapping subcomponents are conforming. It means that they have the same optimum value with respect to both subcomponent functions. It can be simply optimized in a standard CC framework.

  2. 2.

    Theoretically, 17 components should also be detected for \(F_1\) but round-off erros affect the results for that particular function.

  3. 3.

    Note that for \(F_2\), the CC-ORDG is stuck in a pseudo-optima for a few runs. It causes the large green-colored area in Fig. 3b.


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Correspondence to Julien Blanchard .

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Blanchard, J., Beauthier, C., Carletti, T. (2021). Investigating Overlapped Strategies to Solve Overlapping Problems in a Cooperative Co-evolutionary Framework. In: Dorronsoro, B., Amodeo, L., Pavone, M., Ruiz, P. (eds) Optimization and Learning. OLA 2021. Communications in Computer and Information Science, vol 1443. Springer, Cham.

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