Abstract
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 language | English |
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Title of host publication | Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation |
Editors | Sara Silva |
Place of Publication | New York NY USA |
Publisher | Association for Computing Machinery (ACM) |
Pages | 313-320 |
Number of pages | 8 |
ISBN (Electronic) | 9781450334723 |
DOIs | |
Publication status | Published - 11 Jul 2015 |
Externally published | Yes |
Event | The Genetic and Evolutionary Computation Conference 2015 - Madrid, Spain Duration: 11 Jul 2015 → 15 Jul 2015 Conference number: 17th http://www.sigevo.org/gecco-2015/ https://dl.acm.org/doi/proceedings/10.1145/2739480 (Proceedings) |
Conference
Conference | The Genetic and Evolutionary Computation Conference 2015 |
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Abbreviated title | GECCO 2015 |
Country/Territory | Spain |
City | Madrid |
Period | 11/07/15 → 15/07/15 |
Internet address |
Keywords
- Cooperative coevolution
- Large scale global optimization
- Problem decomposition
- Variable interaction