Methoden vergleichen

	Optimierung mittels Surrogaten	Design of Experiments
Fachgebiet	Optimierung	Versuchsplanung
Familie	Process / pipeline	Process / pipeline
Entstehungsjahr	1989 (computer experiments formulation)	1935
Urheber	Sacks, Welch, Mitchell & Wynn (computer experiments framework, 1989); Kriging popularised by Matheron (1963)	Ronald A. Fisher
Typ	Metamodel-assisted black-box optimization	Experimental planning framework
Wegweisende Quelle	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.
Aliasnamen	Vekil Model Tabanlı Optimizasyon (Surrogate-Based), metamodel-assisted optimization, surrogate modelling, emulator-based optimization	DOE, experimental design, factorial experimentation, planned experimentation
Verwandt	5	3
Zusammenfassung	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.