Extended differential grouping for large scale global optimization with direct and indirect variable interactions

Yuan Sun, Michael Kirley, Saman K. Halgamuge

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearch

69 Citations (Scopus)


Cooperative co-evolution is a framework that can be used to effectively solve large scale optimization problems. This approach employs a divide and conquer strategy, which decomposes the problem into sub-components that are optimized separately. However, solution quality relies heavily on the decomposition method used. Ideally, the interacting decision variables should be assigned to the same sub-component and the interdependency between sub-components should be kept to a minimum. Differential grouping, a recently proposed method, has high decomposition accuracy across a suite of benchmark functions. However, we show that differential grouping can only identify decision variables that interact directly. Subsequently, we propose an extension of differential grouping that is able to correctly identify decision variables that also interact indirectly. Empirical studies show that our extended differential grouping method achieves perfect decomposition on all of the benchmark functions investigated. Significantly, when our decomposition method is embedded in the cooperative co-evolution framework, it achieves comparable or better solution quality than the differential grouping method.

Original languageEnglish
Title of host publicationProceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation
EditorsSara Silva
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Number of pages8
ISBN (Electronic)9781450334723
Publication statusPublished - 11 Jul 2015
Externally publishedYes
EventGenetic and Evolutionary Computation Conference 2015 - Madrid, Spain
Duration: 11 Jul 201515 Jul 2015
Conference number: 16th


ConferenceGenetic and Evolutionary Computation Conference 2015
Abbreviated titleGECCO 2015
OtherThe Genetic and Evolutionary Computation Conference (GECCO 2015) will present the latest high-quality results in genetic and evolutionary computation. Topics include: genetic algorithms, genetic programming, evolution strategies, evolutionary programming, memetic algorithms, hyper heuristics, real-world applications, evolutionary machine learning, evolvable hardware, artificial life, adaptive behaviour, ant colony optimization, swarm intelligence, biological applications, evolutionary robotics, coevolution, artificial immune systems, and more.
Internet address


  • Cooperative coevolution
  • Large scale global optimization
  • Problem decomposition
  • Variable interaction

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