Сравнение на методи

	Генетичен алгоритъм	Симулирано отгряване	Tabu Search
Област	Оптимизация	Оптимизация	Оптимизация
Семейство	Process / pipeline	Process / pipeline	Process / pipeline
Година на възникване	1975	1983	1989
Създател	John Henry Holland	—	Fred Glover
Тип	Population-based metaheuristic	Probabilistic metaheuristic / local search	Local-search metaheuristic
Основополагащ източник	Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press.	Kirkpatrick, S., Gelatt, C.D. & Vecchi, M.P. (1983). Optimization by Simulated Annealing. Science, 220(4598), 671-680.	Glover, F. (1989). Tabu Search — Part I. ORSA Journal on Computing, 1(3), 190–206.
Други названия	GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon	Benzetimli Tavlama (Simulated Annealing), SA, probabilistic local search	Tabu Araması (Tabu Search), TS, tabu metaheuristic
Свързани	5	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.	Simulated annealing is a probabilistic local-search metaheuristic introduced by Kirkpatrick, Gelatt, and Vecchi in 1983. It models the physical annealing process in metallurgy — where a material is heated and then slowly cooled to reach a low-energy crystalline state — and uses this analogy to escape local optima in combinatorial and continuous optimization problems.	Tabu Search is a local-search metaheuristic introduced by Fred Glover in 1989 that uses a tabu list — a short-term memory of recently visited solutions — to prevent cycling and escape local optima. By explicitly forbidding moves that reverse recent decisions, the algorithm explores the search space more broadly and, through long-term memory structures such as aspiration criteria, aims to approach the global optimum even in large, complex combinatorial problems.