Comparar métodos

	Metodología Híbrida de Superficie de Respuesta	Algoritmo Genético
Campo	Diseño experimental	Optimización
Familia	Process / pipeline	Process / pipeline
Año de origen	1990s–2000s (systematic hybrid applications)	1975
Autor original	Box & Wilson (RSM foundation, 1951); hybrid extensions by various authors from the 1990s onward	John Henry Holland
Tipo	Optimization methodology	Population-based metaheuristic
Fuente 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	Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press.
Alias	Hybrid RSM, RSM-hybrid optimization, combined RSM, meta-model hybrid optimization	GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon
Relacionados	5	5
Resumen	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.	A genetic algorithm (GA) is a population-based metaheuristic optimization method introduced by John Henry Holland (1975) that mimics the principles of natural selection. It maintains a population of candidate solutions and iteratively improves them through selection, crossover, and mutation operators, making it especially powerful on discontinuous, non-convex, and multi-modal search spaces where classical gradient-based methods fail.