Migration Synchronous Genetic Algorithm for Reverse Engineering

Much research adopts various evolution computation technologies to identify system parameters and structures of highly nonlinear S-system model. They always focus on evolution skills and neglect that the choice of performance index is the key for learning. A suitable performance index not only provides good searching direction but also reduces computation time. In this study, a migration synchronous genetic algorithm (MSGA) is proposed for achieving global optimal search. Twenty eight performances of concentrationor slope-error-based indexes for parameter identification is examined and discussed. When the chosen performance candidates are used for structure identification, only oneor two-steps pruning-operation is necessary. The pruning threshold is set to be -15 10 to ensure a safely pruningaction is guaranteed positively. Keywords-Inverse engineer; parameter estimation; evolution computation; genetic algorithm.

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