Сравнение на методи

	Генетичен алгоритъм	NSGA-II
Област	Оптимизация	Оптимизация
Семейство	Process / pipeline	Process / pipeline
Година на възникване	1975	2002
Създател	John Henry Holland	—
Тип	Population-based metaheuristic	Evolutionary multi-objective optimisation algorithm
Основополагащ източник	Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press.	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.
Други названия	GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon	NSGA2, Non-dominated Sorting GA II, NSGA-II — Çok Amaçlı Evrimsel Optimizasyon
Свързани	5	4
Резюме	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.	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.