Usporedite metode

	Hiperheuristike	Genetički algoritam	Tabu Search
Područje	Optimizacija	Optimizacija	Optimizacija
Obitelj	Process / pipeline	Process / pipeline	Process / pipeline
Godina nastanka	2013	1975	1989
Tvorac	Burke et al.	John Henry Holland	Fred Glover
Vrsta	High-level search methodology	Population-based metaheuristic	Local-search metaheuristic
Temeljni izvor	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.	Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press.	Glover, F. (1989). Tabu Search — Part I. ORSA Journal on Computing, 1(3), 190–206.
Drugi nazivi	Heuristic of Heuristics, Algorithm Selection Hyper-Heuristic, Selection Hyper-Heuristic, Hiyer-Sezgisel	GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon	Tabu Araması (Tabu Search), TS, tabu metaheuristic
Srodne	3	5	4
Sažetak	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.	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.	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.