A supervisory architecture and hybrid GA for the identifications of complex systems

Genetic Algorithms (GA's) have been demonstrated to be a promising search and optimization technique. However, there are two issues regarding applying genetic algorithms to complex system identifications. The first issue is the high computational cost due to their slow convergence. The second issue is its scalability to deal with high dimensional model identification problems. To alleviate the difficulties, we propose a two-layer supervisory model optimization architecture and hybrid GA algorithms. The upper supervisory layer guides the low level optimization algorithm so that the optimization space of the algorithm is gradually reduced. The lower layer uses simplex-GA approach to perform search and numerical optimization within the range defined by the upper layer. Simplex is added as an additional operator of traditional GA to speed up the convergence. We have applied the proposed approach to tomographic reconstruction and the modeling of central metabolism, the results are satisfactory.