Real-coded ECGA for solving decomposable real-valued optimization problems

This paper presents the real-coded extended compact genetic algorithms (rECGA) for decomposable real-valued optimization problems. Mutual information among real-valued variables is employed to measure variables interaction or dependency, and the variables clustering and aggregation algorithms are proposed to identify the substructures of a problem through partitioning variables. Then, mixture Gaussian probability density function is estimated to model the promising individuals for each substructure, and the sampling of multivariate Gaussian probability density function is done by adopting Cholesky decomposition. Finally, experiments on decomposable test functions are conducted. The results show that the rECGA is able to correctly identify the substructure of decomposable problems with linear or nonlinear correlations, and achieves a good scalability.