Multidimensional time series feature engineering by hybrid evolutionary approach

Evolutionary Algorithms (EA) have been successfully employed for solving difficult constrained engineering optimization problems. However, EA implementations often suffer from premature convergence due to the lack of proper balance between exploration ...

Hybrid evolutionary algorithms (EAs) are effective optimization methods that combine multiple EAs. We propose several hybrid EAs by combining some recently-developed EAs with a biogeography-based hybridization strategy. We test our hybrid EAs on the ...

The behavior of standard evolutionary algorithm in the case of multi-modal optimization problems meets a major difficulty. It generally converges towards a single optimum point failing to maintain in the population the multiple optima of the problem ...