Combining a neural network with a genetic algorithm for process parameter optimization

Abstract A neural-network model has been developed to predict the value of a critical strength parameter (internal bond) in a particleboard manufacturing process, based on process operating parameters and conditions. A genetic algorithm was then applied to the trained neural network model to determine the process parameter values that would result in desired levels of the strength parameter for given operating conditions. The integrated NN–GA system was successful in determining the process parameter values needed under different conditions, and at various stages in the process, to provide the desired level of internal bond. The NN–GA tool allows a manufacturer to quickly determine the values of critical process parameters needed to achieve acceptable levels of board strength, based on current operating conditions and the stage of manufacturing.

[1]  Lawrence. Davis,et al.  Handbook Of Genetic Algorithms , 1990 .

[2]  Nostrand Reinhold,et al.  the utility of using the genetic algorithm approach on the problem of Davis, L. (1991), Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York. , 1991 .

[3]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[4]  Munirpallam A. Venkataramanan,et al.  Genetic search and the dynamic facility layout problem , 1994, Comput. Oper. Res..

[5]  Deborah F. Cook,et al.  Genetic algorithm approach to a lumber cutting optimization problem , 1991 .

[6]  Laura Ignizio Burke.  Introduction to artificial neural systems for pattern recognition , 1991, Comput. Oper. Res..

[7]  Stephen F. Smith,et al.  Using Genetic Algorithms to Schedule Flow Shop Releases , 1989, ICGA.

[8]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[9]  Deborah F. Cook,et al.  Predicting the internal bond strength of particleboard, utilizing a radial basis function neural network , 1997 .

[10]  C. Fonseca,et al.  GENETIC ALGORITHMS FOR MULTI-OBJECTIVE OPTIMIZATION: FORMULATION, DISCUSSION, AND GENERALIZATION , 1993 .

[11]  Alain Delchambre,et al.  A genetic algorithm for bin packing and line balancing , 1992, Proceedings 1992 IEEE International Conference on Robotics and Automation.

[12]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[13]  Stephanie Forrest,et al.  Proceedings of the 5th International Conference on Genetic Algorithms , 1993 .

[14]  Peter J. Fleming,et al.  Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization , 1993, ICGA.