Trade-offs in optimization of GMDH-type neural networks for modelling of a complex process

Authors:
N. Nariman-Zadeh;E. Haghgoo;A. Jamali
Affiliations:
Faculty of Mechanical Engineering, Islamic Azad University, Takestan Branch, Iran;Faculty of Mechanical Engineering, Islamic Azad University, Takestan Branch, Iran;Faculty of Mechanical Engineering, Islamic Azad University, Takestan Branch, Iran
Venue:
ISTASC'06 Proceedings of the 6th WSEAS International Conference on Systems Theory & Scientific Computation
Year:
2006

Citing 3
Cited 0

Self-Organizing Methods in Modeling: Gmdh Type Algorithms

Self-Organizing Methods in Modeling: Gmdh Type Algorithms
Genetic diversity as an objective in multi-objective evolutionary algorithms

Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II

IEEE Transactions on Evolutionary Computation

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Abstract

Evolutionary Algorithms (EAs) are deployed for multi-objective Pareto optimal design of Group Method of Data Handling (GMDH)-type neural networks that have been used for modelling of a complex process (such as explosive cutting process) using some input-output experimental data. In this way, EAs with a new encoding scheme is firstly presented to evolutionary design of the generalized GMDH-type neural networks in which the connectivity configurations in such networks are not limited to adjacent layers. Multi-objective EAs (non-dominated sorting genetic algorithm, NSGA-II) with a new diversity preserving mechanism are secondly used for Pareto optimization of such GMDH-type neural networks. Optimal Pareto fronts are obtained which exhibit the trade-off between pair of conflicting objectives and, thus, provide different nondominated optimal choices of GMDH-type neural networks models for such complex process.