방법 비교

	불확실성 하에서도 좋은 성능을 유지하는 해 찾기: 강건 모의 담금질	강건 유전 알고리즘
분야	시뮬레이션	시뮬레이션
계열	Process / pipeline	Process / pipeline
기원 연도	1983 (SA); robust variant emerged 1990s–2000s	2005 (systematic survey); earlier applications from late 1990s
창시자	Kirkpatrick, Gelatt & Vecchi (SA basis); robust formulation developed across the operations research community	Jin, Y. and Branke, J. (systematic formalization); roots in Holland (1975)
유형	Metaheuristic with robustness evaluation	Metaheuristic evolutionary optimizer with robustness mechanism
원전	Kirkpatrick, S., Gelatt, C. D., Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598), 671-680.	Jin, Y., Branke, J. (2005). Evolutionary optimization in uncertain environments — a survey. IEEE Transactions on Evolutionary Computation, 9(3), 303–317.
별칭	RSA, Robust SA, Uncertainty-robust simulated annealing, Worst-case simulated annealing	RGA, Robust GA, Uncertainty-Aware Genetic Algorithm, Noise-Tolerant Genetic Algorithm
관련	5	6
요약	Robust Simulated Annealing (RSA) adapts the classical simulated annealing metaheuristic to seek solutions that perform well not just under nominal conditions but across the full range of uncertain or adversarial parameter values. By embedding a robustness evaluation — worst-case, expected-case, or regret-based — into the SA acceptance step, RSA trades some nominal optimality for resilience, making it valuable when problem parameters are imprecisely known or subject to environmental variation.	The Robust Genetic Algorithm (RGA) extends standard genetic algorithms to find solutions that perform well not only at the nominal design point but also when subjected to uncertainty in decision variables, parameters, or fitness evaluations. By incorporating explicit robustness measures into selection pressure, RGA balances optimality against sensitivity to perturbation, making it suitable for engineering design, scheduling, and policy optimization under real-world variability.