Comparer des méthodes

	Optimisation basée sur les modèles de substitution	Planification d'Expériences
Domaine	Optimisation	Plans d'expériences
Famille	Process / pipeline	Process / pipeline
Année d'origine	1989 (computer experiments formulation)	1935
Auteur d'origine	Sacks, Welch, Mitchell & Wynn (computer experiments framework, 1989); Kriging popularised by Matheron (1963)	Ronald A. Fisher
Type	Metamodel-assisted black-box optimization	Experimental planning framework
Source fondatrice	Forrester, A., Sobester, A., & Keane, A. (2008). Engineering Design via Surrogate Modelling: A Practical Guide. Wiley.	Fisher, R. A. (1935). The Design of Experiments. Oliver and Boyd.
Alias	Vekil Model Tabanlı Optimizasyon (Surrogate-Based), metamodel-assisted optimization, surrogate modelling, emulator-based optimization	DOE, experimental design, factorial experimentation, planned experimentation
Apparentées	5	3
Résumé	Surrogate-based optimization, formalized in the computer-experiments framework of Sacks et al. (1989) and popularized for engineering by Forrester et al. (2008), replaces a prohibitively expensive simulation or physical experiment with a cheap approximate model — called a surrogate or metamodel — and then optimizes that surrogate instead. The surrogate is typically a Kriging (Gaussian Process), Radial Basis Function, or polynomial response surface fitted to a small set of carefully chosen design evaluations and periodically updated as the search progresses.	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.