Comparer des méthodes

	Méthodologie de Surface de Réponse Hybride	Planification d'Expériences
Domaine	Plans d'expériences	Plans d'expériences
Famille	Process / pipeline	Process / pipeline
Année d'origine	1990s–2000s (systematic hybrid applications)	1935
Auteur d'origine	Box & Wilson (RSM foundation, 1951); hybrid extensions by various authors from the 1990s onward	Ronald A. Fisher
Type	Optimization methodology	Experimental planning framework
Source fondatrice	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	Fisher, R. A. (1935). The Design of Experiments. Oliver and Boyd.
Alias	Hybrid RSM, RSM-hybrid optimization, combined RSM, meta-model hybrid optimization	DOE, experimental design, factorial experimentation, planned experimentation
Apparentées	5	3
Résumé	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.	Design of Experiments (DOE) is a systematic framework for planning, conducting, and analyzing controlled experiments to determine how multiple input factors simultaneously affect one or more responses. Introduced by Ronald A. Fisher in 1935, DOE allows researchers and engineers to identify causal relationships, quantify factor effects, and find optimal settings efficiently — using far fewer runs than one-factor-at-a-time approaches. It is foundational in engineering, manufacturing, agriculture, and applied sciences.