Compara mètodes

	Metodologia Híbrida de Superfícies de Resposta	Metodologia de Superfície de Resposta (RSM)
Camp	Disseny experimental	Disseny experimental
Família	Process / pipeline	Hypothesis test
Any d'origen	1990s–2000s (systematic hybrid applications)	1951
Autor original	Box & Wilson (RSM foundation, 1951); hybrid extensions by various authors from the 1990s onward	George E. P. Box & K. B. Wilson
Tipus	Optimization methodology	Second-order polynomial response surface model
Font seminal	Myers, R. H., Montgomery, D. C., & Anderson-Cook, C. M. (2016). Response Surface Methodology: Process and Product Optimization Using Designed Experiments (4th ed.). Wiley. ISBN: 978-1118916032	Box, G. E. P. & Wilson, K. B. (1951). On the experimental attainment of optimum conditions. Journal of the Royal Statistical Society, Series B, 13(1), 1–45.
Àlies	Hybrid RSM, RSM-hybrid optimization, combined RSM, meta-model hybrid optimization	RSM, Central Composite Design, Box-Behnken Design, CCD
Relacionats	5	7
Resum	Hybrid Response Surface Methodology (Hybrid RSM) couples classical response surface designs — which fit low-order polynomial approximations of a system response — with a secondary optimizer such as a genetic algorithm, particle swarm, or artificial neural network. The combination overcomes RSM's limitation of assuming smooth, near-quadratic response landscapes by letting the surrogate model be explored globally, making it widely used in engineering process optimization, product design, and simulation-based studies.	Response Surface Methodology is a collection of statistical and mathematical techniques for building an empirical second-order polynomial model that relates a continuous response variable to two or more controllable input factors, and then locating the factor settings that optimize that response. The approach was introduced by George E. P. Box and K. B. Wilson in their landmark 1951 paper and has since become a cornerstone of process optimization across engineering, chemistry, food science, and pharmaceutics.