Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
A fuzzy approach for multiresponse optimization: an off-line quality engineering problem
Fuzzy Sets and Systems
Information Sciences—Informatics and Computer Science: An International Journal
Proceedings of the 3rd International Conference on Genetic Algorithms
An Analysis of the Interacting Roles of Population Size and Crossover in Genetic Algorithms
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy
Evolutionary Computation
An overview of evolutionary algorithms for parameter optimization
Evolutionary Computation
Predictive models for the breeder genetic algorithm i. continuous parameter optimization
Evolutionary Computation
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
NeuroFAST: on-line neuro-fuzzy ART-based structure and parameterlearning TSK model
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Change point determination for a multivariate process using a two-stage hybrid scheme
Applied Soft Computing
D-FNN based soft-sensor modeling and migration reconfiguration of polymerizing process
Applied Soft Computing
Sensor deployment for fault diagnosis using a new discrete optimization algorithm
Applied Soft Computing
The evolutionary development of roughness prediction models
Applied Soft Computing
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Determination of process conditions for a fluid dispensing process of microchip encapsulation is a highly skilled task, which is usually based on engineers' knowledge and intuitive sense acquired through long-term experience rather than on a theoretical and analytical approach. Facing with the global competition, the current trial-and-error approach is inadequate. Modelling the fluid dispensing process is important because it enables us to understand the process behaviour, as well as determine the optimum operating conditions of the process for a high yield, low cost and robust operation. In this research, modelling and optimization of fluid dispensing processes based on neural fuzzy networks and genetic algorithms are described. First, neural fuzzy networks approach is used to model fluid dispensing process for microchip encapsulation. An N-fold validation tests were conducted. Results of the tests indicate that the mean errors and variances of errors of the modelling based on the neural fuzzy networks approach are all better than those of the other existing approaches, statistical regression, fuzzy regression and neural networks, on modelling the fluid dispensing. It is then followed by the determination of process conditions of the process based on a genetic algorithm approach. Validation tests were conducted. Results of them indicate that process conditions determined based on the proposed approaches can achieve the specified quality requirements.