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Next, an optimization model with genetic algorithm method is developed to optimize the process.
For better accurate forecasting of port throughput, a back propagation neural network model with genetic algorithms is proposed.
The prediction accuracy of optimized neural network model with genetic algorithm had a root mean-squared error of 0.03 nm/min.
This work presents a method for optimising the design of thermally insulated boxes for lowest cost (while ensuring that product temperatures are maintained within set limits) by combining a heat transfer model with genetic algorithm optimisation.
The experimental results obtained from a statistical experimental design were used for the modeling and optimization by linking an artificial neural network (ANN) model with genetic algorithm (GA) in MATLAB.
The first proposed method integrates a cellular automata (CA) simulation model with genetic algorithm optimization method.
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In quantitative genetics, Markov chain Monte Carlo (MCMC) methods are indispensable for statistical inference in non-standard models like generalized linear models with genetic random effects or models with genetically structured variance heterogeneity.
In response to these two extremes, the authors propose a new approach called Meta-Parametric Design, combining graph-based parametric modelling with genetic programming.
Until recently, our laboratories and others have developed mouse models with genetic deficiencies in glutamate-cysteine ligase (GCL), the rate-limiting enzyme in the GSH biosynthetic pathway.
The proposed model has been the result of coupling two RSM models with genetic algorithm.
For example, in the models with genetic alterations, although the proteins encoded by the genes of human and rodents are highly homologous, the regulation of gene transcription by transcription factors or epigenetics, or the regulation of gene translation by microRNAs, can vary extensively between humans and rodents.
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