Võrdle meetodeid

	Genetiline algoritm	Matheuristics	Tabu otsing
Valdkond	Optimeerimine	Optimeerimine	Optimeerimine
Perekond	Process / pipeline	Process / pipeline	Process / pipeline
Tekkeaasta	1975	2009	1989
Looja	John Henry Holland	Maniezzo, Stützle & Voß	Fred Glover
Tüüp	Population-based metaheuristic	Hybrid optimization framework	Local-search metaheuristic
Algallikas	Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press.	Maniezzo, V., Stützle, T., & Voß, S. (Eds.). (2009). Matheuristics: Hybridizing Metaheuristics and Mathematical Programming. Springer. ISBN: 978-1-4419-1305-0	Glover, F. (1989). Tabu Search — Part I. ORSA Journal on Computing, 1(3), 190–206.
Rööpnimetused	GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon	Hybrid Metaheuristics, MIP-based Heuristics, Math-Programming Hybrids, Matematiksel Sezgisel Yöntemler	Tabu Araması (Tabu Search), TS, tabu metaheuristic
Seotud	5	3	4
Kokkuvõte	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.	Matheuristics is a class of hybrid optimization methods that tightly couple exact mathematical programming components—such as mixed-integer programming (MIP) solvers—with metaheuristic search procedures. Formally introduced and named by Maniezzo, Stützle, and Voß in 2009, the framework leverages the global-search capability of metaheuristics and the structural exploitation of mathematical programming to tackle large-scale combinatorial optimization problems that neither approach can solve effectively alone.	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.