مقایسهٔ روش‌ها

	الگوریتم مرتب‌سازی غیرمغلوب ژنتیکی II (NSGA-II)	الگوریتم ژنتیک
حوزه	بهینه‌سازی	بهینه‌سازی
خانواده	Process / pipeline	Process / pipeline
سال پیدایش	2002	1975
پدیدآور	—	John Henry Holland
نوع	Evolutionary multi-objective optimisation algorithm	Population-based metaheuristic
منبع بنیادین	Deb, K., Pratap, A., Agarwal, S. & Meyarivan, T. (2002). A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182-197.	Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press.
نام‌های دیگر	NSGA2, Non-dominated Sorting GA II, NSGA-II — Çok Amaçlı Evrimsel Optimizasyon	GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon
مرتبط	4	5
خلاصه	NSGA-II (Non-dominated Sorting Genetic Algorithm II) is the standard reference algorithm for multi-objective evolutionary optimisation, introduced by Deb, Pratap, Agarwal and Meyarivan in 2002. Rather than collapsing multiple conflicting objectives into a single score, it evolves a population of candidate solutions across generations and returns a set of Pareto-optimal trade-off solutions — the Pareto front — using fast non-dominated sorting and a crowding distance metric to preserve diversity.	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.