Сравнение на методи

	Генетичен алгоритъм	Хипер-евристики	Tabu Search
Област	Оптимизация	Оптимизация	Оптимизация
Семейство	Process / pipeline	Process / pipeline	Process / pipeline
Година на възникване	1975	2013	1989
Създател	John Henry Holland	Burke et al.	Fred Glover
Тип	Population-based metaheuristic	High-level search methodology	Local-search metaheuristic
Основополагащ източник	Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press.	Burke, E. K., et al. (2013). Hyper-heuristics: A survey of the state of the art. Journal of the Operational Research Society, 64(12), 1695–1724.	Glover, F. (1989). Tabu Search — Part I. ORSA Journal on Computing, 1(3), 190–206.
Други названия	GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon	Heuristic of Heuristics, Algorithm Selection Hyper-Heuristic, Selection Hyper-Heuristic, Hiyer-Sezgisel	Tabu Araması (Tabu Search), TS, tabu metaheuristic
Свързани	5	3	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.	Hyper-heuristics are high-level methodologies that search over a space of heuristics rather than directly over the space of solutions. Introduced systematically by Burke et al. (2013) in their landmark survey, hyper-heuristics operate by selecting or generating low-level heuristics to solve hard combinatorial optimisation and search problems, aiming to automate the design of optimisation algorithms across diverse problem domains without requiring deep problem-specific knowledge.	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.