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- W2049658624 abstract "Abstract The flow fields computed for a typical continuous caster are analysed using the basic concepts of Pareto‐optimality in the context of multi‐objective optimization. The data generated by the flow solver FLUENT™ are trained through Evolutionary Neural Networks that emerged through a Pareto‐tradeoff between the complexity of the network and its accuracy of training. A number of objectives constructed this way are subjected to optimization using a Multi‐objective Predator‐Prey Genetic Algorithm. The procedure is repeated using the software modeFRONTIER™ and the results are compared and analysed." @default.
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- W2049658624 date "2010-03-01" @default.
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- W2049658624 title "Analyzing the Fluid Flow in Continuous Casting through Evolutionary Neural Nets and Multi-Objective Genetic Algorithms" @default.
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- W2049658624 doi "https://doi.org/10.1002/srin.200900128" @default.
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