Cooperative Co-evolution for Large Scale Optimization with Dynamic Variable Grouping via Marginal Product Modeling

Cooperative co-evolution (CC) algorithm is a promising method to deal with large scale optimization (LSGO) problem. One major challenge in CC is to design a good decomposition strategy to decompose original problem into subproblems. Linkage learning is a technique to search the linkage betweens deci...

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Bibliographic Details
Published in2018 IEEE Congress on Evolutionary Computation (CEC) pp. 1 - 6
Main Authors Wu, Yapei, Peng, Xingguang, Xu, Demin
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2018
Subjects
Collaboration
cooperative coevolution
Couplings
Genetic algorithms
Heuristic algorithms
Large scale optimization
linkage learning
Optimization
Sociology
Statistics
variable grouping
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Summary:Cooperative co-evolution (CC) algorithm is a promising method to deal with large scale optimization (LSGO) problem. One major challenge in CC is to design a good decomposition strategy to decompose original problem into subproblems. Linkage learning is a technique to search the linkage betweens decision variables. In this paper, we proposed a dynamic online grouping CC algorithm which employ the linkage learning method of extended compact genetic algorithm (ECGA). This linkage learning method build marginal product models of population and search for models according to minimum description length (MDL) criterion. A discretization technique and normalization method of MDL value is used to make the linkage learning more adaptive in large scale optimization. The decision variables will be regrouped according to the linkage learned when the optimization procedure is considered as stagnant. Experiments are conduct on CEC'10 LSGO benchmark function. The test results confirm the validity of this method.
DOI:10.1109/CEC.2018.8477740