Krahasoni metodat

	Algoritëm Gjenetik	Matheuristics: Bashkimi i Programimit Matematik me Metaheuristikat
Fusha	Optimizimi	Optimizimi
Familja	Process / pipeline	Process / pipeline
Viti i origjinës	1975	2009
Krijuesi	John Henry Holland	Maniezzo, Stützle & Voß
Lloji	Population-based metaheuristic	Hybrid optimization framework
Burimi themelues	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
Emërtime të tjera	GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon	Hybrid Metaheuristics, MIP-based Heuristics, Math-Programming Hybrids, Matematiksel Sezgisel Yöntemler
Të lidhura	5	3
Përmbledhja	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.